# Parabola - Full Content > Parabola is a workflow automation platform that lets operations teams automate manual data tasks without code. > This file contains only public, published content from the site. --- # 10 Manual Workflows That Are Costing Your Ops and Finance Teams Weeks Every Month Source: https://parabola.io/blog/10-manual-workflows-that-are-costing-your-ops-and-finance-teams-weeks-every-month Ask any operations or finance leader what's eating their team's time, and you'll hear the same answers: reconciling data across systems, closing the books, chasing down freight invoices, compiling reports from five different sources. These aren't niche edge cases. They're the norm. Across dozens of conversations with ops and finance teams at consumer brands, e-commerce companies, and manufacturers, the same ten pain points surface again and again — and they're almost always rooted in the same underlying cause: disconnected systems, unstructured data, and workflows that were built around spreadsheets. This post breaks down the most common manual workflows we see slowing teams down, why they're so persistent, and what it looks like when companies start solving them at the source. ## 1. Manual Data Reconciliation and Matching This is the most common pain point we hear — and for good reason. Matching invoices to purchase orders to contracts (three-way match), reconciling inventory across an ERP, WMS, and 3PL, aligning cash receipts, and validating sales tax figures are all tasks that sound simple in theory and are brutal in practice. The challenge isn't that teams don't understand the process. It's that the data lives in different systems, arrives in different formats, and needs to be pulled, cleaned, and compared manually every single time. A mismatch means tracking down a paper trail across multiple platforms. One off-cycle run can eat an entire day. For finance teams specifically, this work is high-stakes and non-negotiable — and yet there's often no tooling to support it beyond Excel. ## 2. Fragmented Data Across Systems Most modern operations teams work across at least four or five systems: an ERP, a WMS, a CRM, carrier portals, retailer portals, and some combination of spreadsheets holding it all together. None of these systems talk to each other natively in a way that's useful. The result: someone has to manually download data from each source, reconcile the formats, compile it into a working view, and then do it all again next week. This is especially acute for teams managing consignment inventory, complex ERP setups, or multi-entity data aggregation — where a single "complete picture" might require pulling from six or more sources. The data isn't missing. It's just everywhere, and getting to it is a full-time job. ## 3. Month-End Close Taking Too Long Five to seven days is a typical close cycle for the teams we talk to. Some take longer. For most, the bottleneck isn't judgment — it's mechanical: downloading reports, building and updating Excel workbooks, creating journal entries, running reconciliations, and doing it in a specific sequence that one or two people know by heart. When a team member leaves or a system changes mid-cycle, that tribal knowledge disappears and close gets even slower. Teams that have recently migrated ERPs or restructured finance operations often see close times balloon, not because the new system is worse, but because all the manual workarounds haven't been rebuilt yet. The goal most teams articulate: cut close from seven days to three or four. The obstacle is always the same volume of manual steps standing between the data and the output. ## 4. Freight and Logistics Invoice Auditing Freight billing is notoriously messy. Carriers invoice in different formats, rates vary by contract and carrier, and any given shipment might pass through multiple hands before a final bill arrives. Manually matching freight invoices to quotes, validating against carrier contracts, and identifying billing discrepancies requires pulling data from multiple sources and comparing it line by line. For companies with high parcel volume, this is a significant financial exposure — overbilling often goes undetected because auditing is too time-consuming to do consistently. Teams that have tried to address this with third-party freight audit providers often find they're still doing substantial manual work on their end to support those providers. ## 5. Lack of Real-Time Visibility A surprisingly large number of teams still operate on monthly or weekly reporting cycles — not because they want to, but because getting current data requires manual effort they don't have bandwidth for every day. Real-time visibility into inbound inventory, shipment status, and unit-level tracking is a common ask. So is the ability to make freight mode decisions (air vs. ocean) with current lead time and cost data. Without live visibility, decisions get made on stale data or gut instinct — and that has downstream costs on inventory positioning, customer commitments, and freight spend. ## 6. Repetitive Excel and Spreadsheet Work This one underlies almost everything else on this list. Excel is the connective tissue of most operations and finance workflows — VLOOKUPs, manual currency conversions, version-controlled workbooks passed between team members, P&L compilation done row by row. The problem isn't that Excel is bad. It's that it doesn't scale, it doesn't have memory, and it requires human intervention every single cycle. Workbooks that took one analyst two hours to build now take a different analyst six hours because the logic isn't documented. Errors compound. Version control becomes its own job. Teams know the work is repetitive. They often can't articulate exactly how many hours it costs because it's so embedded in how they operate — it just feels like "how finance works." ## 7. Order Management and Processing Wholesale purchase orders arrive as PDFs. EDI documents need manual validation. Amazon Vendor Central orders require manual entry downstream. PO changes and cancellations come in via email and need to be tracked and reconciled against what's already been processed. Order management is an area where the volume of manual work scales directly with the business — more customers, more channels, more complexity. Teams managing multi-channel order flows often have one or two people whose entire job is essentially data entry and exception handling. That's not a scalable model. ## 8. KPI Reporting and Dashboarding "Death by a million dashboards" is a phrase we've heard more than once. Compiling KPIs from multiple sources for S&OP meetings, executive reporting, or vendor scorecarding is time-consuming enough that some teams describe it as "crippling" — the work required to produce the report takes longer than the discussion the report is meant to support. Vendor scorecarding in particular is an underserved workflow: pulling 3PL or carrier performance data, normalizing it across providers, and presenting it in a consistent format requires a manual data pipeline that most teams rebuild from scratch each reporting cycle. ## 9. Deductions and Chargebacks Management Retailer deductions and chargebacks are a constant source of friction for brands selling through wholesale or retail channels. Reconciling deductions, disputing invalid early payment discounts, and managing chargeback workflows requires pulling data from multiple retailer portals — each with a different format and different logic — and comparing it against internal records. The financial impact is real and often underreported because the effort required to dispute invalid deductions exceeds what teams have bandwidth for. Deductions that should be recovered go uncontested because the manual work isn't worth it at current scale. ## 10. Accruals Automation Month-end accruals are a judgment-intensive but largely mechanical process: identifying open invoices, tracking non-PO spend, and generating accrual entries across accounts. Teams that manage this in Excel are doing work that could be systematized — but the variability in how invoices arrive and how spend is categorized makes it hard to automate without a flexible data layer. Teams working inside procurement platforms like Ariba face a particular challenge: the platform generates the data, but extracting and transforming it into a usable accrual format still requires significant manual intervention. ## The Common Thread Every one of these pain points shares the same DNA: manual, repetitive work caused by disconnected systems and data that arrives in formats that require human intervention to be useful. The teams experiencing these problems aren't behind the curve. They're using modern ERPs, WMS platforms, and cloud tools. The gap isn't the systems themselves — it's the connective tissue between them. Data pipelines that move, transform, and reconcile information across systems are what's missing, and spreadsheets are the duct tape filling that gap. The teams making progress are the ones treating these workflows as engineering problems, not staffing problems. Instead of adding headcount to manage reconciliation volume, they're building repeatable, automated pipelines that run the same logic every cycle — without manual intervention. That shift — from spreadsheet-as-process to pipeline-as-process — is where the leverage is. --- # Becoming Legible to AI Source: https://parabola.io/blog/becoming-legible-to-ai Most leaders spent the holidays either burying their heads in the sand or doom-scrolling AI announcements with a case of Sunday scaries about the ground shifting underneath them. A few came back ready to win, focused on becoming legible to AI. Can an AI agent actually understand how your business works? Does it know what counts as a qualified lead, which edge cases matter, and where the authoritative answer lives when finance and sales disagree? This is the bottleneck. Despite billions in investment, [only 6% of organizations are AI high performers](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) achieving significant value, with nearly two-thirds remaining stuck still just experimenting. The AI models are capable and ready, most companies just haven’t done the work to get the context of how their businesses actually run available to those models. Will Manidis [wrote](https://minutes.substack.com/p/on-becoming-legible-to-capital) about companies becoming "legible to capital." The more legible they become to pools of capital, the more dollars form behind them. What’s hard for most companies becomes incredibly easy for them. Same is true here. Some businesses are highly legible to AI. New models are released, and the next day these businesses are already achieving an order of magnitude more productivity. Their teams are on podcasts talking about how they’ve scaled revenue 5x without adding headcount. And their results are real. So what does it take to be legible to AI? 1. **Business logic is explicit.** Edge cases documented. Rules written down. The answer isn't "ask Sarah." 1. **Context is connected.** Data from your CRM, ERP, internal tools, and spreadsheets are able to be combined. If each system is an island, the agent is blind. 1. **Knowledge compounds.** Every decision is encoded and becomes available to the next person and the next agent. None of this is actually about AI. These are the same things that make organizations work for humans. Clear documentation. Connected systems. Institutional memory that doesn't walk out the door. AI just raises the stakes. Companies that invested in operational rigor are discovering they’ve already built the right foundation for AI. Just ask people like Nick at Ro, Dani at Fabletics, Josh at Magic Spoon, or Marie at Whoop. Companies that run on tribal knowledge are discovering the AI transformation they want has a prerequisite they haven't met. Vendors are promising AI benefits everywhere. But if they're not giving you surface area to encode how your business works, they can't deliver. It’s like a brilliant employee who never onboards into your way of working. ([Foundation Capital](https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity/) calls this missing layer the "context graph.") AI can do remarkable things today. It can already change the entire operating model of your business. Even more remarkable things are coming this year. But you’ll only benefit if you're legible. The companies that figure this out won't just use AI. They'll compound with it. --- # How WHOOP Uses Parabola to Automate Manual Work Across Operations Source: https://parabola.io/blog/how-whoop-uses-parabola-to-automate-manual-work-across-operations ‍ At **WHOOP**, automation has taken hold across operations, inventory, finance, and even internal AI enablement. Not through a single initiative, but through a series of practical workflows built by the people closest to the work. The result has been less manual effort, fewer errors, and more time spent on analysis and decision-making rather than data prep. ### Giving operators the ability to fix their own problems *Marie Fodnis, Director of Global Operations* ### Spending less time preparing data, more time understanding it *Nate Fry, Apparel & Accessories Inventory Analyst* ### Using automation to make AI adoption measurable *Ryan Durkin, VP of AI at Work* ### A practical pattern for operational automation That’s the role Parabola plays for teams like WHOOP’s. It provides a way to turn manual processes into reliable systems without forcing operators to hand their work off to engineering or wait for long implementation cycles. When automation fits naturally into how teams already think about their work, adoption follows—and the benefits show up quickly in accuracy, speed, and focus. --- # Introducing The State of AI in Operations: 2025 Research Report Source: https://parabola.io/blog/introducing-the-state-of-ai-in-operations-2025-research-report When we talk to operations teams, we keep hearing the same thing: "We're using AI." But when we dug deeper into what that actually meant, we found something surprising. While 98% of operations teams are using or experimenting with AI today, most are still in the experimental phase. Only 10% have AI in most workflows or as core to how they operate. So we decided to find out why. We surveyed hundreds of operations professionals to understand where AI adoption really stands, what's holding teams back, and where teams see the clearest opportunities for value. The results reveal a clear picture: AI in operations isn't a question of "if" anymore, but "how fast." [Download the full State of AI in Operations: 2025 Research Report →](https://parabola.io/ai-in-operations-report) ## Who we surveyed and how we conducted this research We surveyed hundreds of operations leaders across industries and company sizes in Q3 2025. Nearly **89% of respondents are directly involved in evaluating or implementing new software and AI tools for their teams**, giving us insights from the people actually making these decisions. ## Key findings ### 1. 98% of operations teams are using AI, but only 10% have scaled beyond experimentation 98% of operations teams are using or experimenting with AI. Only 1.7% said they're not using it at all. But here's the reality of current adoption: - 52% are using AI in some workflows - 36% are experimenting with small projects - Only 10% have AI in most workflows or as core to operations When we asked teams to self-assess their AI maturity, 45% consider themselves intermediate (using AI for a few repeatable workflows), while only 21% have reached the advanced stage where AI creates real competitive advantage. **The takeaway:** We're past the early adopter phase. The majority of operations teams have moved from "should we try AI?" to "how do we do this better?" ### 2. Trust and training are bigger barriers to AI adoption than budget Here's what surprised us most: only 9% of operations teams cite cost as their biggest barrier to AI adoption. The real blockers are: - 41% cite trust and accuracy concerns - 36% cite lack of expertise or training - Only 9% cite cost/budget limitations Trust and training concerns outweigh cost by nearly 5 to 1. Meanwhile, 71% plan to increase their AI budgets in the next 12 months, with 21% planning significant increases. **The takeaway:** Teams want to use AI. They have budget for AI. They can see the potential value. But they need to trust it will work correctly and they need to know how to use it effectively. This creates a clear opportunity for solutions that prioritize reliability, transparency, and user education. ### 3. Data entry and cleaning delivers the highest ROI from AI in operations When we asked what would deliver the highest ROI from AI, the answer was overwhelmingly clear: - 41% said data entry and cleaning - 17% said reporting - 16% said forecasting - 13% said reconciliation Data-centric work represents 87% of priority use cases. **The takeaway:** Operations teams are focused on solving their most time-consuming, manual tasks first. Data entry and cleaning isn't glamorous, but it's where teams spend countless hours that could be better used elsewhere. Starting with data work also builds confidence, since the results are easy to measure and verify. ### 4. 68% expect AI to become industry standard in operations within 1-3 years 68% of operations leaders expect AI to be standard in their industry within the next 3 years. But here's the urgent part: **37% expect it to be standard in the next 12 months.** Only 14% believe it's already standard, which means there's a narrow window for competitive advantage. **The takeaway:** This isn't speculation about the distant future—it's active planning for the immediate roadmap. Teams that don't have a clear AI strategy risk being left behind, not in years, but in months. ## What this means for operations leaders Operations teams are moving beyond the experimental phase and into practical implementation. They have clear priorities (data work), realistic timelines (1-3 years to become standard), and growing budgets (71% increasing spend). But they're stuck at a critical juncture—between experimentation and scale. They've proven AI works in small projects, but struggle to move those wins into production workflows they can trust when it matters most. AI adoption in operations isn't a future trend—it's happening right now. With most teams expecting AI to become standard within 12-24 months, timing is critical for building competitive advantages. **Key takeaways:** - **Start with data entry and cleaning for quick wins** that build confidence and free up time - **Invest in training and change management, not just technology** since the barrier is confidence, not budget - **Choose solutions that prioritize accuracy and transparency** to address the trust concerns holding teams back - **Act quickly—the timeline is months, not years** with 37% expecting AI to be standard within 12 months The window for competitive advantage is closing fast. While 98% of teams have started the journey, only 21% have reached the destination where AI creates real competitive advantage. [Download the complete State of AI in Operations: 2025 Research Report →](https://parabola.io/ai-in-operations-report) — **About the research:***This report is based on survey responses from hundreds of operations professionals across industries and company sizes, conducted in Q3 2025.* --- # Stress Test Your Peak Plan: Operational Readiness for the Holiday Surge Source: https://parabola.io/blog/stress-test-your-peak-plan-operational-readiness-for-the-holiday-surge Every year, the same story plays out: brands coast into October feeling confident, then watch their operations buckle under the weight of Black Friday and Cyber Monday. Orders flood in. Systems that hummed along in July suddenly grind to a halt. Manual processes that worked fine at 100 orders a day collapse at 1,000. The difference between brands that thrive during peak season and those that merely survive isn't budget or headcount. It's preparation. The best operators treat Q4 like a marathon runner treats race day: they train past the conditions they expect, stress-test every system, and build flexibility into their operations before the starting gun fires. We sat down with operations leaders at brands like On Running, Faherty, and Skims to understand what actually breaks first, how to prevent it, and how to turn the holiday surge into a catalyst for long-term operational excellence. Here's what we learned. ## What breaks first (and how to prevent it) The first cracks almost always appear in data flow between systems. When order volumes spike, any weak link in the data chain can snowball quickly. A slightly off-format file from a 3PL, a carrier delay that isn't flagged fast enough, or a single mis-keyed SKU triggers a chain reaction: wrong labels, late shipments, angry customers. For smaller teams without a 3PL, the same risk lives in the handoffs between Shopify, shipping software, and the packing station. If you're still relying on manual spreadsheet uploads or someone double-checking inventory counts, those steps simply don't scale. **Think of it like training for a marathon.** Runners don't just run 26.2 miles once and call it good—they build endurance and train past race conditions so the big day feels manageable. Your operations should work the same way: push your systems beyond the peak you expect. Run larger test volumes, feed in messy data, and make sure every connection holds up so the first surprise doesn't expose a weak spot. The smartest operators stress-test their handoffs early (returns processing, order routing, carrier reconciliation) so they know their systems can handle five or ten times the normal load. Don't wait until November to discover your breaking points. ## Data as infrastructure, not an afterthought Treating data as core infrastructure changes everything. In practice, that means every system—your storefront, ERP, shipping tools—feeds accurate, real-time information into one clean flow. The most prepared brands set up automated workflows that pull and transform data as it changes, creating a single source of truth for orders, inventory, and service levels. When volumes spike, they're not digging through yesterday's spreadsheets. They're making decisions based on live numbers they trust. **Being able to act on data starts with the hygiene of that data.** Clean data flowing automatically between systems means fewer downstream errors and better customer experiences when the stakes are highest. The holiday surge exposes every crack in disconnected systems: Shopify, 3PLs, carriers, ERPs. Parabola acts as connective tissue, making sure data flows cleanly across tools so those cracks don't become chasms. ## Where to start with AI: Find your time drains When we talk to operators about AI, the most common question is: "Where do I even start?" The answer is simpler than you might think: **look for the manual, tedious work that's eating up your time.** Ask yourself: What am I doing repeatedly that makes me think, "There has to be a better way"? Where am I copying and pasting data between systems? What tasks spike during busy periods and turn into bottlenecks? A great starting point is something simple but powerful: [use AI to extract or parse data from PDFs](https://parabola.io/blog/parsing-pdfs-with-parabola). Set up a workflow that reads a shipment PDF and outputs a clean table in the same format every time. It's a low-lift way to turn messy documents into structured data you can actually use. From there, operators get more ambitious. We've seen customers build AI-driven workflows for tariff scenario modeling, invoice reconciliation, and exception summarization. These are back-office processes where AI quietly saves time and money, even if they aren't flashy. Here's the reality check: MIT's Media Lab found that [**95% of AI pilot projects fail to deliver measurable financial impact](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/).** Not because the technology doesn't work, but because leaders don't embed AI where it can create real value. Too often, companies run marketing experiments while ignoring the operational tasks where the biggest savings live. There are countless AI products out there, and depending on what you're looking to do, Parabola can be a great start. We've published templates of our top AI workflows (the same ones used by On Running, Faherty, and Skims) so even a team of fewer than ten people can drop them in and start automating right away. **The key is experimentation.** Start small, prove the value in a single workflow, and build from there. By the time peak hits, those workflows are trained and reliable, not experimental. ## Build for flexibility: Your Q4 contingency plan Peak season always throws a curveball: carrier delays, a SKU that sells out, or a promo that blows up bigger than you planned. You can't predict every twist, but you can design your operations so you can pivot fast. A simple example: maintain a single source of truth for orders and inventory (even a well-structured Google Sheet works). Then set clear decision rules: backup carriers, adjustable cut-off times, a short "if X happens, do Y" checklist. When something pops up, you're not scrambling. Parabola gives teams the ability to adapt workflows quickly, without needing to wait on engineering. That agility lets you redirect orders, change rules, or spin up new reports mid-season when surprises hit. The goal is to spot issues early and pivot quickly, so you're never stuck while customers are waiting on you. ## Lessons beyond Q4: Turn peak into long-term advantage Treat the holiday rush as a free stress test for your entire operation. Every bottleneck you spot is a chance to automate and improve for the long term. Document what broke, capture the fixes, and turn those into standard workflows so they pay off in Q1 and beyond. Experiment with AI now, not later. Over the past year we've seen engineering teams adopt AI quickly; the next wave will be operations and supply-chain teams—the people spending hours moving data around in spreadsheets. Tools for parsing and cleaning data can take a lot of that manual work off your plate. If you use the lessons from this peak season to test and refine those workflows, you'll start the new year with systems that are faster, cleaner, and ready to scale. The pressure test of holiday surge often exposes where systems are weakest. Brands that document and automate fixes carry those efficiencies forward. Holiday season becomes not just survival, but a catalyst for operational maturity. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––‍ **Ready to stress-test your operations before the surge hits?** Explore our library of [pre-built workflows](https://parabola.io/use-cases) and [AI templates](https://parabola.io/use-cases?ai=true) used by leading brands, or join our [community of operators](https://parabola.io/resources/the-sop-community) sharing peak season strategies. --- # AI in Supply Chain: From Skepticism to Strategic Necessity Source: https://parabola.io/blog/ai-in-supply-chain-from-skepticism-to-strategic-necessity In a recent survey – [The Supply Chain Tech Stack Report](https://parabola.io/the-supply-chain-tech-stack-report#the-state-of-supply-chain), conducted by [Parabola](https://parabola.io/) and StartOps in early 2025 on 90 supply chain leaders at leading brands – a revealing statistic stood out: 0% of respondents think AI is overhyped. This unanimous acknowledgment of AI's importance signals a dramatic shift in how brands view artificial intelligence in operations. ## Current State of AI Adoption The 2025 Supply Chain Tech Stack Report reveals widespread AI implementation amongst the respondents: - 52% are already using AI-powered tools - 35% are planning to implement AI - 54% view AI as "essential" to future operations - Only 14% have no plans to implement AI solutions ## The AI Implementation Gap While the majority of brands cited moving toward AI adoption, there's a notable contrast between current implementation and future opportunities: - Customer service leads current adoption - AI/ML for demand forecasting (55%) tops future investment priorities - Advanced supply chain visibility (53%) follows as a key opportunity - The shift suggests a move from customer-facing to core operational uses ## Understanding Adoption Barriers Despite clear benefits, implementation challenges remain. As Two Boxes CEO Kyle Bertin notes, "If the false positive here is very costly for brands, and they're afraid of it, that can be a disastrous experience." The hesitation to embrace AI stems from several key concerns, particularly around accuracy in critical business processes. ## Starting Small with AI Matthew Hertz, CEO of Third Person, advocates for an incremental approach: "I've used Claude to pop in a 3PL agreement and said, 'Can you identify any concerns in this agreement?' And it spit out three or four things that I should challenge them on...and this is all free." Obvi CEO Ronak Shah adds, "I think using it for any place where there is a repetitive service input has been huge. Even just identifying places that we should improve gives us a lot of ideas — if I can have the brain of 10,000 robots, we can get a lot more ideas coming through." ## Looking Ahead: AI's Critical Role As Paige Zachs, VP of Supply Chain, Ops, and Customer at Coterie warns: "It's one of those technologies that the later you start, the further behind you're gonna be." While AI adoption is inevitable, Keith Frymark emphasizes an important balance: "The human element will always be there." *Based on a*[comprehensive survey](https://parabola.io/the-supply-chain-tech-stack-report#the-state-of-supply-chain)*of 90 supply chain and operations leaders conducted in early 2025 by Parabola and StartOps.* ‍ --- # Supply Chain Visibility in 2025 and Beyond Source: https://parabola.io/blog/supply-chain-visibility-in-2025-and-beyond When [asked about the biggest opportunities](https://parabola.io/the-supply-chain-tech-stack-report#the-state-of-supply-chain) for their companies in 2025 in a survey conducted by Parabola, supply chain leaders pointed to two key areas: AI/ML for demand forecasting (55%) and advanced supply chain visibility tools (53%). This focus comes as no surprise, with 51% citing limited visibility as a top pain point in their current tech stacks. ## The Visibility Challenge "Visibility continues to be a huge gap for brands and 3PLs...the whole ecosystem," explains Matt Hertz, CEO of Third Person. "Planning is the hardest function of the ecommerce business. You look at what a planner goes through: these Bibles of Excel workbooks, and it's just a lot of guesses." ## Defining Supply Chain Visibility Supply chain visibility encompasses multiple aspects: - Package tracking - Cross-channel inventory insight - Payment tracking and reconciliation - Vendor management ## AI's Role in Supply Chain Visibility Hertz poses a compelling question: "Why not use computers who are better than humans at gauging trends and forecasting?" StartOps founder Charles Cushing points to platforms like Four Kites and Project 44 as examples of tools paving the way for more supply chain visibility and management with AI. ## Current Tech Stack Landscape The survey revealed varied adoption rates across company sizes: - Large enterprises ($250M-$1B+) typically use enterprise-level ERPs - Mid-sized companies ($11M-$250M) often combine ERPs with specialized tools - Smaller companies ($1M-$10M) rely more on standalone solutions ## Looking Ahead The data shows three key principles will define successful tech stack strategies in 2025: 1. Embrace AI or fall behind 1. Prioritize integration and visibility 1. Build for scale, not just for today Success will come to organizations that can: - Make strategic bets on the right tools at the right time - Focus relentlessly on integration and automation - Measure and prove ROI to justify further investment - Build internal expertise alongside their tech stack *Based on*[a comprehensive survey of 90 supply chain and operations leaders](https://parabola.io/the-supply-chain-tech-stack-report#the-state-of-supply-chain)*conducted in early 2025 by*[Parabola](https://parabola.io/)*and StartOps.* --- # The Automation Paradox: Why 41% of Brands Still Don't Use Workflow Tools Source: https://parabola.io/blog/the-automation-paradox-why-41-of-brands-still-dont-use-workflow-tools A surprising finding from the [2025 Supply Chain Tech Stack Report](https://parabola.io/the-supply-chain-tech-stack-report) reveals a puzzling contradiction in supply chain operations. Despite integration being cited as the top pain point by 60% of brands, 41% of companies still don't use any workflow automation platform. The report, which surveyed 90 supply chain leaders and included in-depth expert interviews, exposes this automation gap as a significant missed opportunity for operational efficiency. ## The Integration Challenge The survey of 90 supply chain leaders revealed that: - 60% cite lack of integration between tools as their primary pain point - 50% struggle with limited visibility or reporting capabilities - Budget constraints are the primary barrier to adoption for 45% of respondents ## Current Automation Landscape Of those who do use workflow automation: - [Parabola](https://parabola.io/) leads with 37% adoption - Make follows at 7% - Other solutions make up the remainder ## Why Teams Resist Automation The reluctance stems from multiple factors: - Budget constraints (45%) - Complexity of implementation (17%) - Lack of internal expertise (17%) Keith Frymark, SVP of Supply Chain and Quality at Seed Health, explains the paradox: "They're just surviving, there are a lot of smaller ops teams where they're running a lean operation. On a small team of maybe 3 or 4 you're forced to do a lot at a fast pace, and you just don't have time to sit down and truly build out workflows." ## The Cost of Waiting [Parabola CEO](https://www.linkedin.com/in/alexyaseen/) Alex Yaseen notes, "If you're constantly 'too busy' to evaluate the technology that could save you hours (or full team headcount), you'll never escape the hamster wheel." ## The Business Case for Automation "I'd much rather rely on someone saying 'Hey, I'm gonna use this tool and it's gonna do the job of two other people,'" says Ronak Shah, CEO of Obvi. "I think in today's climate I'm much more like, 'Let's move fast, let's go and get whatever we can use to forge ahead.'" Coterie's Paige Zachs reinforces this view: "We're trying to grow our business by 50% this year without increasing our headcount… you do that by hiring only the best and investing in tech." *Based on*[a comprehensive survey](https://parabola.io/the-supply-chain-tech-stack-report#the-state-of-supply-chain)*of 90 supply chain and operations leaders conducted in early 2025 by*[Parabola](https://parabola.io/)*and StartOps.* --- # The State of Supply Chain Tech: Why Only 7% of Brands Are "Very Satisfied" With Their Stack Source: https://parabola.io/blog/the-state-of-supply-chain-tech-in-2025-why-only-7-percent-of-brands-are-very-satisfied In a recent survey – [The Supply Chain Tech Stack Report](https://parabola.io/the-supply-chain-tech-stack-report#the-state-of-supply-chain), conducted by Parabola and StartOps in early 2025 on 90 supply chain leaders at leading brands – a stark reality was revealed: only 7% of brands report being "very satisfied" with their current technology solutions. This dissatisfaction stems from a fundamental tension between the need to innovate and significant resource constraints. ## Key Pain Points Plaguing Supply Chain Tech Survey results revealed that integration remains the biggest challenge for supply chain leaders, with 60% of respondents citing lack of integration between tools as their primary pain point. Additionally, 50% struggle with limited visibility or reporting capabilities. When breaking down satisfaction by company size: - Larger companies ($250M+) tend to be more satisfied overall - Smaller companies (<$50M) show more varied satisfaction levels - Mid-sized companies ($50M-$250M) seem to struggle the most with their tech stacks ## Budget Constraints Drive Conservative Tech Adoption Cost pressures are creating a challenging environment for technology investment, as found in th survey results: - 45% cite budget constraints as the primary barrier to adoption - Team bandwidth emerged as a critical limiting factor - Mid-sized companies ($50M-$250M) struggle most with tech satisfaction "There's so much cost pressure on brands right now. Tariffs, the cost of 3PL services, cost of goods going up — there's so many things strapping budgets," explains Kyle Bertin, CEO and co-founder of Two Boxes. ## Integration Challenges Persist "You have certain supply chain platforms that are slam dunks, that fully automate things and take you out of Google sheets or manual tracking," explains Paige Zachs, VP of Supply Chain, Ops and Customer at Coterie. "But then you have other tools that are overly complex where even basic integrations become a huge challenge." ## Finding ROI in a Resource-Constrained Environment Keith Frymark, Senior Vice President of Supply Chain and Quality at Seed Health, suggests that ops teams are well-positioned to justify tech investments: "I think supply chain and ops teams have the most leverage in the business — $20,000 doesn't scare me, I can save that tomorrow." He recommends teams struggling to secure tech investment focus on highlighting savings they create in other areas that can fund new tools. "Renegotiate a contract, shave off 10 cents on packaging, look at your tariff codes, reclassify something…we have so much ability to save money." ## The Path Forward Success in 2025 and beyond will come to organizations that can make strategic technology investments while building the internal expertise to leverage them effectively. The challenge isn't just selecting the right tools — it's creating an integrated technology ecosystem that can scale efficiently while delivering clear ROI. Three key principles will define successful tech stack strategies: 1. Embrace AI or fall behind 1. Prioritize integration and visibility 1. Build for scale, not just for today *Based on a*[comprehensive survey](https://parabola.io/the-supply-chain-tech-stack-report)*of 90 supply chain and operations leaders conducted in early 2025 by*[Parabola](https://parabola.io/)*and StartOps.* --- # Introducing the Parabola Experts Program Source: https://parabola.io/blog/introducing-the-parabola-experts-program It started with a simple pattern. We’d wrap up a customer call and almost always hear the same question: *“Do you know someone who can help us build this?”* At the same time, we kept seeing names pop up in shared flows. Consultants. Boutique agencies. Fractional operators. All using Parabola to deliver results for their clients. That’s what sparked the idea. Today, we’re launching the Parabola Experts program: a curated network of hands-on operators, consultants, and agencies who specialize in building better systems for scaling teams. [→ Meet the experts and learn more](https://parabola.io/experts) ## What it is The Experts program connects brands with experienced operators who specialize in supply chain, automation, systems integration, and more. These are people who’ve done the work before and know how to move quickly. It also gives experts more visibility, more opportunities, and new ways to grow their business. Here’s how it works: - **For experts**: You’ll be listed in our public directory and introduced to warm leads. You’ll earn rev share on referrals and get opportunities to co-create content. - **For brands**: You’ll find experts with experience across different tools, teams, and systems. Some already use Parabola, others don’t—but all know how to jump in and help with whatever you need. ## Why now Operations is more complex than ever. Teams are lean. Expectations are high. And the people solving these problems don’t always sit in-house. More brands are turning to outside experts for help with planning, system design, automation, and analytics. The Experts program makes that easier. It helps brands find the right partner and gives operators the tools to create even more leverage. ## What’s ahead This is the first step. We’ll be surfacing expert-picked templates, sharing real stories of how these operators work, and continuing to invest in the people behind the processes. If you’re looking for someone to help you move faster—or want to be that someone—we’d love for you to take a look. → [Explore the Experts program](https://parabola.io/experts) Cheers, Alex --- # A Language, AI-Powered IDE, and Serverless Runtime for Spreadsheet-Style Logic Source: https://parabola.io/blog/ai-language-ide-and-serverless-runtime-for-spreadsheet-style-logic Every engineer has tried to save an ops team from spreadsheet hell. We show them Python. We build them dashboards. We promise a better way. And they always, always go back to spreadsheets. For the past seven years, I’ve been obsessed with solving this problem. And it’s been so much harder than I expected. ## There’s a hidden crisis in every company Watch any operations or finance team for a day and you'll see some scary stuff: - Copy-pasting from emails into Excel - Using cmd + f to one-by-one go through a long list of find/replace rules - VLOOKUPs referencing random Google Sheets that break when someone renames a column - Eyeballing PDFs to manually enter data - Hardcoding values when systems don't talk to each other - Redoing everything when an exec questions why a number doesn't match This is how critical business processes like inventory reconciliation, invoice auditing, and GL mapping actually work at most companies. But this isn't incompetence. These processes change too fast and handle too many messy data edge cases to be solved with code. The people doing this work have deep domain knowledge and know exactly what needs to happen. They just lack proper tools. ## We tried to solve it differently than traditional solutions RPA tools break when UIs change. No-code platforms hit walls with real-world data complexity. BI tools assume your data is already clean. These existing tools just weren’t designed to solve these kind of problems. So we set out with a fundamentally different approach: an entire engineering stack-equivalent designed for how business teams actually think about data. - ‍**A domain-specific language** that operates on data the way someone thinks in spreadsheets, not SQL or Python. When an ops person says "match these two lists but ignore blanks and typos," it just works as expected.**‍** - **A visual IDE with live debugging** where every transformation shows instant previews. Users see exactly what's happening to their data at each step without having to wait until the end to “run and pray”**‍** - **A schema-less calculation engine** that handles the reality of business data—CSVs that gain columns overnight, five different date formats in one file, random strings where numbers should be.**‍** - **Production-grade infrastructure** including simple abstractions for statefulness, serverless deployment, inbound email triggering, comprehensive logging, and exception handling that actually helps non-technical users fix what went wrong. ## But the technology just wasn’t there yet Building all of this got us partway there. We managed to close some impressive logos I was proud of, but adoption was still limited to power users who could translate business logic into visual flows. Seven years in, we'd built a domain specific language, IDE, and serverless runtime. And most people were still going back to spreadsheets. Then this year, LLMs reached a tipping point. And we finally added two features we should have had all along: - ‍**An AI agent that actually understands operations**. It's trained on thousands of real workflows and can build with you or for you. Ask it to "reconcile these invoices but flag anything over 5% variance" and it knows exactly what you mean.**‍** - **Dynamic code generation that adapts**. Instead of brittle scripts, Parabola generates fresh code on the fly that updates when your data shape changes. The code is visible, auditable, and modifiable—no black boxes. In hindsight, we were incredibly naive to try to tackle this problem before 2025-grade LLMs. I could justify it to myself that AI only works when it has the right foundation. Without our domain-specific language and tooling, this would just be another chatbot that generates broken Python. But the reality is it doesn’t matter if it was well planned or just good timing. Because regardless, ops teams are now prompting their way to production-grade automations. ## Getting an AI agent to actually understand ops problems LLMs in 2025 are increasingly great at reasoning through problems step by step, and surprisingly good at generating code to solve those problems. They have massive amounts of code to train on. There’s far less available training data for ops and finance workflows, because so much lives in people’s heads or in private docs. So we had to build a lot of new tools for our AI agent to use that give it the ability to - ‍**Manipulate Parabola flows** by adding/removing steps, adding/removing connections, updating settings, etc.**‍** - **Look up info** in our public facing docs, internal docs, and templates**‍** - **Get schema information** for each step type and compare proposed changes with preconfigured examples**‍** - **Access context** the user wants to share like error messages and selections**‍** - **Communicate** back/forth with the user in structured ways And to make these tools work well, we had to create a lot of carefully tuned content - ‍**Partial Parabola flows** demonstrating important patterns and efficient ways of solving hard problems**‍** - **End-to-end templates** for our most popular use cases**‍** - **Schemas and sample data** sets for all of our integrations and transforms**‍** - **A compilation** of the internal docs, looms, and siloed knowledge our cx teams use internally into parseable docs**‍** - **Examples** of how to know when a user is asking a question about a use case vs. asking to actually make a change [happy to share the technical details if anyone’s curious, just DM me] ## Technical teams still need to help upskill their business teams Ops and finance teams need to work like engineering teams. With repeatability, scale, and an increasing adoption of AI. I’m hopeful Parabola can be a big part of the solution. But they need help from their technical counterparts to get there. They need encouragement and guidance on what problems are worth tackling first. It might feel like taking on extra work, but it’s SO worth it on the other side. Fewer “quick python scripts”, more auditable processes, and the ability to scale without hiring are all clear ROI. But what gets me out of bed in the morning is the raw excitement ops and finance people have the first time they build something real that solves a problem they’ve been handling manually for years. I’d love to hear what you and your teams think. You can [try Parabola here](https://parabola.io/), or [see some example flows](https://parabola.io/use-cases). --- # Introducing the New Parabola Source: https://parabola.io/blog/introducing-the-new-parabola Most people don’t realize just how much operators are responsible for. They’re the ones building the processes that keep everything moving—connecting tools, maintaining systems, unblocking teams, and laying the foundation for growth. They’re often behind the scenes, but their work is front and center when it matters most. And they’ve always been asked to do more with less. We built Parabola for them. Teams at Brooklinen, Caraway, On Running, Flexport, and hundreds more use Parabola to automate the kind of work they thought would always be manual, like auditing invoices, reconciling data across systems, or exporting the same report every Monday. They’re transforming messy data from PDFs, emails, and spreadsheets into clean, reliable workflows—without writing code or waiting on engineering. It has quietly become the engine behind some of the most operationally excellent teams in the world. And it’s high time for everyone to be able to see those same results. Today, customers use Parabola’s drag-and-drop interface and meticulously crafted steps to build intelligent workflows that mirror exactly how their business should run. They have full control over how data is cleaned, transformed, and automated. Now, we’ve rebuilt the entire experience from the ground up. It’s 100x faster to go from idea to outcome—and radically easier to get started. This is the new Parabola. The clearest expression of what we’ve always set out to build. At its core is a new AI-powered chat interface that makes building feel conversational. Instead of building everything by hand, you just describe what you want to automate in plain language—and Parabola turns that intent into a working flow. It sets up the structure, configures the steps, and gets you to a finished product in minutes. It feels natural because it matches how we now expect software to work. The rise of ChatGPT and LLMs didn’t just introduce new tools. It introduced a new way of thinking. We’ve gotten used to working in prompts, describing what we want, and expecting the system to handle the complexity. That shift is now shaping how we expect every tool to work. AI isn’t optional anymore. It’s a baseline expectation. But most AI tools overpromise and underdeliver. They sound impressive, but when it comes time to actually do the work, they fall short—because they don’t understand your use cases, your data, or the complexity behind real operations. We’ve taken a different approach. We’ve spent years learning the workflows, the data sources, and the edge cases that make operations hard to automate. That knowledge is built into Parabola—from the templates and best practices you can use on day one to the carefully designed steps that show you exactly what the AI is doing, every step of the way. So instead of vague suggestions, you get workflows that reflect real logic. And the ability to adjust, fine-tune, and stay in control. After getting it in the hands of some of our favorite customers, here’s what they told us they’re most excited about: - **You don’t need to know where to start.** Just describe what you’re trying to build. Parabola translates your intent into a working flow, directly on the canvas. - **You stay in control.** Parabola doesn’t act on your behalf. You review every step, approve every change, and always understand what’s happening. - Y**ou can ask questions and get real help.** From “why isn’t this working?” to “how do I fix it?” to “can you explain what this Flow does?”—you get answers grounded in your actual workflow, not a generic doc. - **It learns from how you work.** Over time, Parabola adapts to your logic, naming conventions, and style. It gets faster and more helpful every time you build. This is AI that actually helps you get things done. Not a chatbot. Not a suggestion engine. A true partner that helps you apply your expertise to solve real problems—faster. The most frustrating thing as an expert isn’t not knowing what to do—it’s knowing exactly what needs to happen, and not having the time to do it. Operators don’t need another tool that leaves them halfway. They need one that helps them finish—with speed, clarity, and control. The future of operations will be built by those who can move fast, automate with confidence, and stay in control. It’ll be built with Parabola. Alex Yaseen*Founder & CEO* --- # Automating Trade Compliance: Technologies Reshaping Supply Chain Management Source: https://parabola.io/blog/automating-trade-compliance-technologies-reshaping-supply-chain-management As trade policies evolve and supply chains grow more intricate, organizations are rapidly reevaluating their approach to trade compliance. The convergence of artificial intelligence, automation technologies, and sophisticated data processing is transforming how businesses navigate international trade requirements. This transformation comes at a crucial moment, as companies grapple with escalating tariffs, increasingly complex regulatory frameworks, and the pressing need to maintain competitive advantage while ensuring compliance. The stakes have never been higher—and the tools have never been more powerful. ## Why automation matters in trade compliance As Parabola CEO Alex Yaseen notes, “Companies are becoming increasingly focused on technology adoption, particularly as tariffs compound existing pressures. They’re looking to automation rather than hiring—it’s a fundamental shift in how businesses approach operations.” This observation comes at a critical time, as trade compliance has become increasingly complex, with constantly changing tariff codes, duties, and regulations. With tariff rates [reaching](https://parabola.io/blog/navigating-the-new-tariff-reality-what-ecommerce-brands-need-to-know) extreme levels—some as high as 145%—and regulatory requirements shifting almost daily, the margin for error in international trade has essentially disappeared. Manual processes no longer suffice for organizations managing international supply chains at scale. The stakes are particularly high as governments worldwide increasingly use tariffs not just for traditional market protection but also as potential revenue-generating mechanisms. ## Understanding automated tariff classification Modern tariff code lookup UPS and other major carriers have revolutionized classification processes. This transformation comes at a crucial time, as organizations grapple with rapid policy changes that can render traditional classification methods obsolete overnight. These platforms integrate with major carriers like DHL tariff systems, offering automated classification and tariff sheet generation for thousands of products across multiple jurisdictions. The technology becomes particularly vital as organizations navigate complex trade barriers, including traditional tariffs, duties, and quota systems, each with their own classification requirements and compliance protocols. ### Benefits of automated classification - Reduced manual entry errors - Automated tariff drawing and documentation - Real-time rate updates - Digital tariff sheet management - Instant recalculation of duties under different trade scenarios - Proactive compliance monitoring across jurisdictions - Historical record maintenance for audit trails - Automated restricted party screening ## Digital transformation of rules tariff management The push toward automation is accelerating faster than many anticipated. “A surprising amount of companies are signing contracts for new technology right now, even amidst otherwise uncertain times,” observes Yaseen. “They’re saying they’ll probably use these tools a lot more than expected because they're going to stop hiring people and just invest in technology.” This shift represents a fundamental change in how organizations approach trade compliance challenges. ### R&L rules tariff automation Transportation providers like R&L have developed sophisticated systems for managing complex tariff rules. These systems have become essential as organizations navigate what many describe as the most challenging trade environment since the 2008 financial crisis. These platforms enable real-time rate calculations, automated route optimization, dynamic cost modeling, and regulatory compliance tracking. The technology has evolved beyond simple rate calculations to incorporate predictive analytics, allowing organizations to model various scenarios and prepare for potential trade policy shifts before they occur. ### SAIA rules tariff implementation SAIA's approach to rules tariff automation [demonstrates](https://www.truckingdive.com/news/saia-centralizes-customer-service-operations/744805/) how carriers are streamlining operations. In an environment where a single misclassification can result in millions in unexpected duties, their system represents a new standard in risk management. Their integrated system combines automated classification, dynamic pricing, compliance verification, and documentation generation. The platform’s ability to adapt to rapid regulatory changes has become particularly valuable as organizations face unprecedented volatility in trade policies and enforcement. ## DHL tariff innovation and integration DHL’s automated tariff management system serves as an industry benchmark, particularly crucial as organizations grapple with complex trade barriers across multiple jurisdictions and unprecedented rates of policy change. Their comprehensive solutions for modern trade management address not just traditional compliance needs but emerging challenges like environmental standards and technology transfer protocols. ### Automated documentation The system handles complex documentation requirements across multiple jurisdictions, including automated tariff drawing and tariff sheet generation, reducing manual processing time and errors. This automation becomes particularly vital as organizations navigate first sale rules, bonded warehouse requirements, and other sophisticated trade mechanisms that require precise documentation and tracking. ### Real-time compliance monitoring Continuous monitoring ensures adherence to regulatory requirements, documentation standards, trade agreement provisions, and restricted party screening protocols. The system’s ability to track changes across multiple jurisdictions simultaneously has become essential as organizations diversify their supply chains beyond traditional manufacturing hubs to mitigate risk. ## Implementing automation solutions ### Technology assessment Organizations should evaluate their current compliance processes, automation opportunities, integration requirements, and ROI potential before implementing new systems. This evaluation must now account for unprecedented market volatility, shifting consumer behaviors, and the potential for dramatic policy changes that can transform entire supply chain strategies overnight. ### Best practices for deployment Successful implementation requires clear process mapping, stakeholder engagement, phased rollout approaches, and continuous monitoring mechanisms. Modern deployments must be particularly agile, allowing organizations to quickly adapt to new trade agreements, changing tariff structures, and evolving compliance requirements across diverse markets. ## System integration considerations ### Connection with existing platforms Modern tariff automation must integrate seamlessly with enterprise systems including ERP, WMS, TMS, and CRM platforms. This integration has become more critical as organizations require real-time visibility into landed costs, compliance status, and potential duty exposure across their entire supply chain network. ### Data management requirements Successful automation depends on robust data architecture. This architecture must support not just current operations but enable sophisticated scenario planning and risk analysis. Organizations need standardized approaches for data collection, classification, information sharing, and compliance documentation. The system must be capable of handling multiple duty scenarios, tracking country of origin requirements, and maintaining detailed audit trails for potential regulatory reviews. ## Security and risk management ### Data protection protocols Trade compliance automation requires strict security measures to protect sensitive information, including: - Proprietary pricing data - Customer information - Regulatory documentation - Commercial agreements - Supply chain diversification strategies - Alternative sourcing plans - Market entry analysis - Competitive intelligence ### Risk mitigation strategies Organizations must [balance](https://gca.isa.org/blog/the-danger-of-overreliance-on-automation-in-cybersecurity) automation benefits against potential risks [SOURCE: Cybersecurity & Infrastructure Security Agency], implementing appropriate controls and monitoring systems. This balance has become more complex as organizations navigate not just traditional compliance risks but emerging challenges like environmental regulations, labor standards, and technology transfer restrictions. ## Change management essentials ### Stakeholder engagement Successful automation requires coordinated effort from compliance teams, IT departments, operations staff, and executive leadership. This coordination must extend beyond traditional organizational boundaries to include suppliers, logistics partners, and even competitors sharing similar trade challenges. ### Training and adoption Organizations should develop comprehensive programs that include initial system training, ongoing education, best practice sharing, and performance monitoring. These programs must now incorporate scenario planning and rapid response protocols to help teams navigate sudden policy changes and market disruptions. ## Building future-ready trade compliance The trade environment as it stands is driving a sea change in how organizations approach technology investment. As Yaseen points out, companies are increasingly viewing technology adoption as non-negotiable: “We’re seeing companies make these investments at a time when they normally wouldn’t, because they recognize automation isn’t just about efficiency—it’s about survival.” Modern trade compliance demands sophisticated automation. As organizations face new challenges in global trade, from dramatic tariff increases to fundamental shifts in supply chain strategies, automation has moved from a competitive advantage to a survival requirement. Success requires selecting the right technologies, implementing them effectively, and maintaining adaptability for future regulatory changes. Organizations that embrace automation position themselves for competitive advantage doing business today. This advantage extends beyond mere compliance to enable strategic opportunities in new markets, alternative sourcing strategies, and innovative business models that can thrive despite trade policy uncertainty. --- # How trade wars impact supply chain operations: a strategic guide Source: https://parabola.io/blog/how-trade-wars-impact-supply-chain-operations-a-strategic-guide ## What is driving the current tariff war with China? The escalating tariff wars between major economies, particularly the US-China trade conflict, have fundamentally altered global supply chains [SOURCE: World Trade Organization]. Recent China tariff updates show continuing tensions, with implications reaching far beyond direct trading partners. ## Understanding China tariff rates in today's market The complexity of current tariff China policies has created unprecedented challenges for supply chain managers. Since 2018, US tariffs on China have affected nearly every industry sector [SOURCE: USTR]. These policies have evolved from simple trade measures into complex regulatory frameworks affecting: Manufacturing costs have risen significantly under new China tariff rates, forcing organizations to reevaluate their sourcing strategies and operational models. ## Trade war impacts on global supply chains The ongoing tariff war has reshaped how organizations approach supply chain management. Beyond immediate cost implications, companies face strategic challenges in supplier relationships, inventory management, and market access. ### Direct operational impacts Recent China tariff updates have forced organizations to confront several critical challenges:• Rising import duties and compliance costs• Supply chain disruptions and shortages• Increased inventory carrying costs These challenges extend beyond simple cost calculations, affecting entire operational structures. ### Strategic implications Organizations navigating the tariff war must balance immediate cost pressures against long-term strategic positioning [SOURCE: Supply Chain Quarterly]. This includes reassessing supplier relationships, exploring alternative markets, and investing in technology solutions. ## Regional shifts and market responses to trade wars ### China trade dynamics As US tariffs on China persist, companies are developing new approaches to manage trade tensions. This includes:• Diversifying supply bases beyond China• Developing regional manufacturing hubs• Investing in automation and technology• Building stronger compliance frameworks ### Alternative market development Companies are increasingly exploring manufacturing options in Southeast Asia, nearshoring possibilities, and domestic production capabilities to reduce dependence on single markets. ## Technology solutions for tariff management Modern trade war navigation requires sophisticated technology solutions for tracking and compliance. Organizations are investing in systems that provide real-time visibility into tariff changes and supply chain impacts. Essential technological capabilities include:• Automated tariff classification and calculation• Real-time compliance monitoring• Predictive analytics for trade pattern shifts ## Looking ahead: preparing for future trade tensions As the tariff war continues to evolve, supply chain leaders must develop flexible, resilient operations. Success requires combining strategic foresight with operational agility, supported by robust technology and diverse supplier relationships. The future of global trade will likely see continued tensions and periodic tariff updates, making adaptability and strategic planning essential for long-term success. ‍ --- # A Supply Chain Leader’s Guide to Trade Restrictions: Tariffs vs. Quotas Source: https://parabola.io/blog/tariffs-vs-quotas-a-supply-chain-leaders-guide-to-trade-restrictions Tariffs and quotas [shaped](https://unctad.org/news/global-trade-hits-record-33-trillion-2024-driven-services-and-developing-economies) the movement of a mind-boggling $33 trillion in global trade in 2024. While both tools restrict trade, their economic impacts differ significantly—tariffs work through price mechanisms while quotas create hard limits on volume. For business leaders managing international supply chains, choosing the wrong strategy to navigate these restrictions can mean the difference between profit and loss. This piece breaks down the key distinctions between tariffs and quotas, examining how each affects business operations, costs, and strategic planning. ## What is the main economic difference between a tariff and a quota? Global trade restrictions come in various forms, but tariffs and quotas [stand](https://www.curtis.com/glossary/international-trade/tariffs#:~:text=Tariffs%20are%20taxes%20that%20governments%20place%20on,of%20good%2C%20or%20with%20a%20specific%20country.) as two of the most significant tools governments use to regulate international commerce. While both mechanisms affect trade flows, they operate through fundamentally different approaches. A tariff is a tax imposed on imported goods, directly affecting prices but allowing unlimited imports if businesses are willing to pay. In contrast, a quota sets a strict limit on the quantity of goods that can enter a country, regardless of market demand or willingness to pay. ## Understanding tariffs: beyond the basic definition ### Revenue tariff definition and purpose A revenue tariff primarily [aims](https://www.congress.gov/crs-product/IF11030#:~:text=Introduction,Among%20this) to generate income for the government rather than protect domestic industries. Unlike protective tariffs, revenue tariffs are typically set at lower rates and applied to goods that aren't produced domestically. ### Two-part tariff systems Some countries implement a two-part tariff structure, combining: - A fixed component (base rate) - A variable component (often based on quantity or value) This system provides flexibility in trade regulation while ensuring minimum revenue generation. ## Quotas: the alternative to tariffs ### How quotas work - Set specific limits on import quantities - Often administered through import licenses - May be allocated among different exporting countries - Can create artificial scarcity in domestic markets ### Embargo vs tariff: understanding the spectrum While tariffs and quotas restrict trade, an embargo represents the extreme end of trade restrictions, completely prohibiting trade with specific countries or in certain goods. Understanding this spectrum helps supply chain leaders navigate various trade barriers effectively. ## Industry-specific applications ### Gas tariff considerations The energy sector provides an excellent example of how tariffs can vary by industry. Gas tariffs often involve: - Complex pricing structures - Seasonal variations - Different rates for industrial vs. residential use ### Automotive sector implications The automotive industry faces unique challenges with tariffs and quotas: - Complex supply chains spanning multiple countries - Steel and aluminum tariff impacts - Component-specific quota restrictions - Electric vehicle battery material considerations - Regional content requirements under trade agreements ### Ecommerce sector considerations The ecommerce industry presents distinct challenges in navigating trade restrictions: - Direct-to-consumer cross-border shipping complexities - Marketplace seller compliance across multiple jurisdictions - Variable duty rates based on product category and value - De minimis thresholds affecting pricing strategies - Return flow implications for international sales ## Strategic implications for supply chain leaders ### Decision-making framework Tariffs and quotas demand fundamentally different strategic responses. While tariffs create predictable cost increases that can be modeled and passed through, quotas introduce supply uncertainty that requires more complex contingency planning. When facing trade restrictions, supply chain leaders should evaluate: For tariff-based restrictions: - Price elasticity of your product line - Cost pass-through potential to customers - Margin impact across different scenarios - Alternative sourcing cost-benefit analysis - Long-term versus short-term cost planning For quota-based restrictions: - Supply guarantees and allocation strategies - Buffer inventory requirements - Secondary supplier qualification timelines - Market share implications - Regional production alternatives ### Risk mitigation strategies Rather than pursuing a one-size-fits-all approach, effective trade restriction management requires matching your strategy to the specific mechanism: Tariff mitigation priorities: - Cost modeling and scenario planning - Supplier contract renegotiation - Product redesign opportunities - Trade agreement qualification - Duty drawback programs Quota mitigation priorities: - Supply allocation guarantees - Market-specific inventory positioning - Alternative market development - Production capacity flexibility - Import license management ## Preparing your supply chain for future trade barriers The key difference between tariffs and quotas lies not just in their economic mechanics, but in how they shape business strategy. Tariffs create predictable cost hurdles that can be measured and managed. Quotas introduce volume constraints that require fundamental changes to supply chain design. Success requires building systems that can adapt to either mechanism. This means developing flexible supplier networks, robust compliance processes, clear cost pass-through protocols, strong market intelligence capabilities, and responsive inventory systems. Organizations that understand these distinctions and build appropriate response mechanisms position themselves to turn trade restrictions from obstacles into competitive advantages. --- # A Guide to Modern Trade Management: Tariffs, Quotas, and Automation Technology Source: https://parabola.io/blog/what-is-a-protective-tariff-a-complete-guide-to-modern-trade-management As global trade enters one of its most volatile periods in recent history, understanding protective tariffs and their impact on business operations has become critical for survival. With tariff rates soaring ([here’s how they compare against previous tariffs](https://time.com/7268866/history-of-tariffs-trump/)) and traditional trade patterns being disrupted, organizations need a comprehensive understanding of both the challenges and opportunities in this new landscape. This guide breaks down the essential elements of modern trade management, incorporating insights from industry leaders. ## What is a protective tariff? A [protective tariff](https://www.wto.org/english/tratop_e/tariffs_e/tariffs_e.htm) is a tax imposed on imported goods specifically designed to shield domestic industries from foreign competition. Historically, governments have used these tariffs strategically and selectively, typically targeting specific industries deemed crucial for national security or economic development, with rates rarely exceeding 25%. While traditionally focused on market protection rather than revenue generation, the current administration has dramatically shifted this paradigm. As Alex Yancher, CEO of Passport Global, [notes](https://parabola.io/blog/navigating-the-new-tariff-reality-what-ecommerce-brands-need-to-know), these tariffs are now being [wielded](https://www.wsj.com/livecoverage/stock-market-tariffs-trade-war-04-04-2025/card/bessent-says-tariff-revenue-could-reach-600-billion-annually-QJfDGCPYDY1C72Ljg1pt) as a powerful revenue-generating mechanism, with ambitious targets to increase revenue from $100 billion in 2024 to between $300-600 billion. ### Protective tariff examples in modern trade In what Brian Burke, Chief Commercial Officer at SEKO Logistics, describes as “absolutely insane” market conditions rivaling both “the global financial crisis and the start of the global pandemic,” recent protective tariffs have fundamentally reshaped global trade. The scale of protective tariffs that has emerged during the Trump administration's trade policies with China shows rates [up to 145%](https://www.nytimes.com/2025/04/10/business/economy/china-tariffs-145-percent.html), fundamentally disrupting decades-old trade patterns. These measures haven’t just altered supply chains—they are forcing a wholesale reimagining of global commerce, sparking widespread manufacturing relocations, and triggering what many experts consider the most significant restructuring of international trade since the 2008 financial crisis. ## The evolution of tariff wars and global trade ### China tariff rates and global impact The current tariff landscape has triggered what Justin Sherlock, CEO of Caspian, calls a “whipsaw” effect in global trade. After a March marked by aggressive stockpiling and strong ocean bookings, the market has lurched to a near standstill as businesses await clarity on tariff rates. Since the escalation of US tariff on China measures during the Trump era, organizations have adapted their supply chain strategies to navigate these challenges. For many businesses, as Izzy Rosenzweig, CEO of Portless observes, the current rates are effectively creating “not a tariff, but an embargo,” forcing fundamental reconsideration of supply chain strategies. ### Beyond China: global tariff considerations While China trade tensions dominate headlines, tariff wars affect multiple regions. This disruption, however, can bring unexpected opportunities. “The tables are turning,” as Burke notes, with emerging markets like Argentina “opening up shop” with a robust middle class ready for US brands. Recent tariff updates have transformed regional trade agreements, reshaped industry regulations, and redefined market access requirements. Organizations must now consider both immediate compliance needs and long-term strategic positioning in this evolving landscape. The challenge, as Rosenzweig emphasizes, isn't just about raising prices: “If you just raise prices by 30%, your conversion rate goes down, then your CAC goes up. So it's not always as simple as the consumer is going to pay the price.” ## Understanding different trade barriers As organizations navigate the complexities of modern trade, success depends on understanding the nuanced differences between various trade barriers. While tariffs dominate current headlines, other mechanisms like duties and quotas play equally crucial roles in shaping global commerce. Here's what decision-makers need to know about these distinct yet interconnected trade barriers: ### Difference between duty and tariff structures The difference between tax and tariff systems lies in their application and purpose. While duties typically apply to specific goods, tariffs often serve broader economic goals. Understanding these distinctions helps organizations navigate complex trade regulations effectively. ### Tariffs vs. quotas: key distinctions Quota systems represent an alternative approach to trade regulation, fundamentally affecting market access, pricing mechanisms, and supply chain planning. Organizations must develop comprehensive strategies that account for both tariff and quota-based restrictions. ## Technology and automation in tariff management With the increasing complexity and speed of trade policy changes, manual approaches to tariff management have become unsustainable. Organizations need sophisticated technological solutions not just to keep pace, but to stay ahead of rapid market shifts and ensure compliance. The stakes are particularly high given that a single misclassification or documentation error can result in significant penalties or delays. Here's how leading organizations are leveraging technology to maintain control: ### Digital documentation and compliance In what Burke describes as “navigating through a haunted house while blindfolded,” modern trade management requires increasingly sophisticated tools for automated tariff drawing and digital tariff sheet processing. Essential capabilities include: - Real-time compliance monitoring - Risk management protocols - Automated documentation - Classification verification ## Industry-specific impacts and adaptations While tariff changes affect all businesses engaged in international trade, their impact varies dramatically across industries. Different sectors face unique challenges based on their supply chain structures, intellectual property considerations, and operational models. Understanding these sector-specific impacts is crucial for developing effective adaptation strategies. Here's how two key industries are responding to the evolving trade landscape: ### Manufacturing sector response “For supply chain manufacturing hubs,” Rosenzweig cautions, “any major shift of manufacturing is a huge risk.” He shares a cautionary tale from the bike industry where companies that hastily relocated manufacturing lost market share to competitors who stayed and gradually adapted. Manufacturing organizations face unique challenges in supply chain restructuring and cost management. Success requires balancing automation implementation with compliance verification while maintaining operational efficiency. ### Technology sector considerations Tech companies navigate additional complexities in intellectual property protection, component sourcing, and cross-border technology transfers. As Yancher points out, even seemingly straightforward solutions like bonded warehouses bring their own complications: “Whatever fulfillment fees you have right now, they're just gonna be way higher in a bonded section of a warehouse because you have to have special certified employees.” Key focus areas include: - Data security requirements - IP protection protocols - Component sourcing strategies - Compliance documentation ## Strategic supply chain adaptation In what Burke calls “the closest to the global financial crisis as well as the start of the global pandemic” in terms of disruption, organizations must fundamentally rethink their approach. Organizations must develop comprehensive approaches to risk mitigation through supplier diversification, geographic risk management, and cost optimization. “This is the time to go global,” Burke advises, emphasizing the importance of looking beyond traditional markets. Success increasingly depends on compliance automation and sophisticated monitoring systems. ## Regional considerations and opportunities ### North American market dynamics Recent policy changes have transformed regional manufacturing patterns and cross-border trade flows. As Sherlock notes, these changes coincide with broader economic pressures: “We’re in a soft consumer environment that we're already in…now the base 10% tariff is going to add some inflation.” Organizations must adapt their strategies to address evolving compliance requirements while optimizing supply chain efficiency. ### Asian market developments Beyond China tariff news, organizations must consider emerging market opportunities, regional trade agreements, and alternative manufacturing hubs. As Burke emphasizes, “You need to look at Argentina, go hard after Europe, go hard after Australia,” suggesting that supply chain diversification has become not just a risk management strategy but a growth opportunity. Supply chain diversification has become essential for long-term success. ## Compliance and documentation requirements The stakes for proper compliance have never been higher. Modern trade compliance demands accurate classification and proper tariff sheet management. Leading organizations leverage automated systems for: - Real-time monitoring - Documentation management - Risk assessment - Compliance verification ## Future trade policy outlook Sherlock suggests approaching the uncertain future with measured steps: “This is the year to find quarters in your couch cushions. This is not the year to take big risks and launch a bunch of new products.” The evolving regulatory landscape continues to reshape international trade. Key areas of focus include digital commerce regulations, environmental standards, and technology transfer protocols. As Burke notes, success requires “navigating through a haunted house while blindfolded,” demanding both caution and strategic foresight. Organizations must maintain flexible strategies to adapt to these emerging requirements. ## Streamlining trade compliance with Parabola Modern trade management demands sophisticated automation solutions. Parabola offers specialized tools designed to address key challenges in international trade compliance and management. ### HTS code classification automation Parabola streamlines product classification by[automating HTS code assignments](https://parabola.io/use-cases/hts-code-classification), ensuring consistency while reducing manual entry errors and compliance risks. ### Tariff scenario modeling Navigate complex trade decisions with[data-driven modeling](https://parabola.io/use-cases/tariff-scenario-modeling) that simulates different scenarios, calculates implications, and provides strategic insights for decision-making. ### Landed cost calculation Accurately determine total import costs through[automated calculations](https://parabola.io/use-cases/landed-cost-calculation) incorporating duties, taxes, transportation fees, insurance costs, and currency conversions. ### CIPL digitization and validation Transform commercial invoice packing list management through[automated data extraction](https://parabola.io/use-cases/cipl-digitization-validation), standardization, and real-time validation. ### Customs document digitization Streamline customs processing through[automated document handling](https://parabola.io/use-cases/customs-document-digitization), data standardization, and compliance verification, enabling faster clearance times. ## Building resilient trade operations Success in modern trade requires understanding what a protective tariff is while developing comprehensive strategies for management. Organizations must balance compliance requirements with operational efficiency, leveraging technology while maintaining flexibility for future changes. The future of global trade demands sophisticated approaches to tariff management, combining strategic foresight with operational excellence and technological innovation. As Burke concludes, “This is not all dark clouds…US companies do have a leg up here in being able to weaponize your supply chain of a more diversified basket of countries, to really go on offense too.” --- # Navigating the New Tariff Reality: What Ecommerce Brands Need to Know Source: https://parabola.io/blog/navigating-the-new-tariff-reality-what-ecommerce-brands-need-to-know Just *how* dramatic are the changes hitting ecommerce right now? We [recently brought together](https://parabola.io/) some of the industry’s leading voices to find out. Their verdict? “This has been absolutely insane,” says Brian Burke, Chief Commercial Officer at SEKO Logistics. “This is as close to the global financial crisis as well as the start of the global pandemic as you can possibly get as far as disruption.” Recent changes to U.S. trade policy are forcing brands to rapidly reassess their supply chain strategies and international operations. With tariff rates [reaching](https://www.cnbc.com/2025/04/21/dropshipping-businesses-china-under-pressure-trumps-tariffs.html) 145% on Chinese goods and fundamental changes to [de minimis rules](https://www.supplychaindive.com/news/de-minimis-future-4-supply-chain-questions/745420/), the industry faces what many experts are calling an unprecedented transformation. The timing of these changes also coincides with challenging consumer conditions. As Justin Sherlock, CEO and founder at duty drawback platform Caspian points out: “Credit’s kind of maxed out right now, charge-off rates on credit cards are at all-time highs, interest rates on credit cards are at all-time highs, mortgage originations are at a low.“ This consumer weakness makes it particularly challenging for businesses to pass on increased costs, requiring more sophisticated strategies for managing the impact of higher tariffs. So what are supply chain and ops leaders to do? There’s really no one-size-fits-all approach. The panel discusses bonded warehouses and free trade zones; going global in pursuit of further diversification; and even sitting back to see where the cards fall before making any quick moves. Here’s what else we know. ## The impact is already visible The effects are already rippling through the industry, creating immediate and dramatic changes in shipping patterns. Ocean bookings from Asia to the U.S. [have plummeted](https://www.cnbc.com/2025/04/16/trade-war-fallout-china-freight-ship-decline-begins-orders-plummet.html) as retailers delay non-critical freight. According Sherlock, “March was a great month for ocean rates and ocean bookings because everyone was trying to stockpile inventory…that’s gonna completely pause and shipping is gonna go on hold for the next month as everyone waits to see what's gonna happen with their tariff rates.“ The impact extends beyond just shipping volumes: Shopify merchants have already begun responding to the anticipated changes, with April 2nd and 3rd seeing the highest price change events of 2025 at the time of the panel, crossing 5 million price adjustments per day. This level of price volatility reflects the immediate response of businesses trying to adapt to the new reality. ## The stakes are high For many businesses, these changes aren’t just about adjusting prices—they’re about survival. Alex Yancher, co-founder and CEO of Passport Global, points to analysis suggesting current tariff rates on Chinese goods are effectively creating “not a tariff, but an embargo.” The implications for businesses are severe, with some facing potential bankruptcy due to unexpected duty obligations. The challenge is particularly acute for businesses with established supply chains in affected regions. “I spoke to one customer that is importing goods for the wholesale business by boat,” shares Izzy Rosenzweig, CEO of Portless. “They’re about to get hit with a close to a million dollars tax bill. They don’t have a million dollars put aside for that tax bill.“ The situation is further complicated by marketing implications. As Rosenzweig explains, “Raising prices isn’t simple, because very often customers are leveraging platforms like Meta or Google or TikTok. If you just raise prices by 30%, your conversion rate goes down, then your CAC goes up. So it’s not always as simple as the consumer is going to pay the price.” ## The revenue strategy behind the changes The panel agreed that understanding the administration’s motivation is crucial for anticipating future developments. As Yancher explains, “We know that this administration is focused on tariffs as a revenue generating source. [They want to cut taxes](https://www.usatoday.com/story/money/2025/04/22/trump-tariffs-replace-income-taxes-economists/83196923007/), do a stimulus, and they want to use this tariff revenue to pay for it.” The target? To [increase tariff revenue](https://www.wsj.com/livecoverage/stock-market-tariffs-trade-war-04-04-2025/card/bessent-says-tariff-revenue-could-reach-600-billion-annually-QJfDGCPYDY1C72Ljg1pt) from $100 billion in 2024 to between $300-600 billion. This dramatic increase implies a fundamental shift in trade policy, with tariff rates potentially expected to rise between 3 to 6 times their current levels. The baseline is anticipated to be approximately 10% minimum, likely ranging between 10% and 20% for most countries, with China facing significantly higher rates. ## Strategic options for brands While the situation is challenging, experts suggest several strategies for brands to consider: ### 1. Diversification of markets “The time to go global is right now,” advises Burke. “You need to look at Argentina, go hard after Europe, go hard after Australia.” The current climate presents an opportunity for U.S. brands to expand internationally rather than solely focusing on domestic markets. Burke emphasizes that this isn’t just about survival but opportunity: “These brands have been selling into the US market for 15 years. It’s not just the big marketplaces out of China. These are big brands that have been selling into the US market for years. They’ve been leveraging their supply chain. Use this as the ‘tables are turning’ opportunity.“ ### 2. Supply chain optimization Several approaches to supply chain optimization are emerging: - **First sale rule implementation**: Rosenzweig details how brands can potentially reduce their duty liability: “Very often factories have a Chinese entity and a Hong Kong entity. First sale law allows you—if you use a third party, usually an accounting firm—to separate some of those line item costs from the factory.“ - **Bonded warehouses and free trade zones**: While these options present opportunities, they come with their own challenges. As Yancher notes, “Whatever fulfillment fees you have right now, they’re just gonna be way higher in a bonded section of a warehouse because you have to have special certified employees.“ - **Duty drawback programs**: Sherlock notes that “it takes several months to get a duty drawback program set up,” but that “these are things that you can do that don't change your operations.” ### 3. Careful planning over rushed decisions The experts advocate for measured responses rather than hasty changes. “For supply chain manufacturing hubs, any major shift of manufacturing is a huge risk,” notes Rosenzweig. He shares a cautionary tale: “There was one brand I was talking to that was in the bike business in 2016 when Trump's first 301 came out. A lot of the competitors moved to other countries. They decided to stay and slowly raise their prices. They end up eating their market share and their business model, because they had the best quality.“ ## Where do we go from here? While uncertainty remains the dominant theme, the consensus among industry leaders is that brands should use this period to educate themselves about alternatives while optimizing their current operations. As Sherlock advises, focus on “things that preserve flexibility and give you paths to cost cutting.” The coming months will be crucial as new policies take effect and the industry adapts to what Burke describes as “navigating through a haunted house while blindfolded.” Success will likely come to those who can balance immediate adaptations with strategic long-term planning, while maintaining the flexibility to respond to further changes in the regulatory environment. --- # 30 Actionable AI Use Cases Across Supply Chain, Finance, and Operations Source: https://parabola.io/blog/30-actionable-ai-use-cases-across-supply-chain-finance-and-operations Most people don’t realize just how much operators are responsible for. They’re the ones building the processes that keep everything moving—connecting tools, maintaining systems, unblocking teams, and laying the foundation for growth. At Parabola, we’ve always built for them. Our workflow builder makes it easy to organize and transform messy data from anywhere—even PDFs, emails, and spreadsheets—so teams can automate the work they thought would always be manual. Today, those teams are doing more than ever with AI. They’re using Parabola’s AI-powered steps to extract unstructured data, standardize inputs from dozens of sources, generate summaries and alerts, and simply create complex logic in their workflows—without writing a line of code. And they’re seeing real, measurable results: lower costs, faster resolutions, cleaner reporting, and better decisions across the business. In this post, we’ll outline the most impactful AI use cases we’ve seen from top operators at companies like Brooklinen, On Running, Caraway, and Flexport—so you can bring the same level of clarity, control, and speed to your team. Continue reading to learn more about actionable use cases across the following steps: 1. Pull from inbound email 1. Standardize with AI 1. Categorize with AI 1. Custom transform 1. Extract with AI 1. Experiment with AI ## Pull from inbound email The [Pull from inbound email](https://parabola.io/product/integration/email-attachment) step is Parabola’s most powerful tool for organizing messy data—whether it's coming in via an unstructured email body or a CSV, Excel, or PDF attachment. With this step, you can use AI to automatically translate messy data into neat tables, which you can then use to build downstream logic. Whether you need to pull line items from an invoice or extract shipment IDs from the body of an email, this step is the go-to tool for operators organizing messy data received via email. Supply chain Enrich TMS, ERP, and other internal systems with complete shipment records Enable real-time shipment visibility across carriers Freight quote request email parsing Email body Extract quote request details from email bodies and triggers Slack/Teams alerts or push request details directly to quoting systems Freight ## Standardize with AI The [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai) step helps teams normalize inconsistent values across systems and vendors. From SKU naming to warehouse locations, this step looks for similar values to automatically clean your data—resulting in improved reporting accuracy, cleaner ERP data, a reduction in sync failures, and improved performance tracking across partners. Use case title Description Team Goal Business impact Normalize column names from vendor-provided files Standardize headers across CSVs, Excels, and PDFs received from third-parties to ensure clean ingestion into ERPs, WMS, or TMS Operations Supply chain Finance Prevent ingestion errors and sync failures Reduce order and invoice ingestion failure rates; improve data integrity Normalize carrier names Standardizes naming across carrier and 3PL reports to enable accurate carrier-level performance tracking Supply chain Build holistic carrier scorecards that take every data source into account By constantly evaluating your carrier network, operators can improve SLA performance and reduce costs. Standardize SKU naming across vendors Aligns SKUs from multiple vendors to a single internal format Operations Ensure accurate inventory and replenishment Reduce inventory integration failures resulting in inaccurate inventory levels across systems. Align invoice terms Standardizes payment terms across vendor invoices Accounting Improve AP consistency and vendor payment strategy Improve cash flow forecasting and early payment discount capture Harmonize product categories Maps vendor category labels to internal taxonomy Operations Enable consistent product-level performance tracking Identify high-margin vs. low-margin categories more effectively Clean store names across reports Unifies store names from various platforms (POS, Shopify, marketplaces) Operations Consolidate store-level reporting Improve store-level P&L analysis and reduce reporting errors ## Custom transform The [Custom transform](https://parabola.io/product/transform/custom-transform) step is Parabola’s most powerful and flexible AI step designed for logic-based data transformation. It enables teams to describe what they want to do in plain language to generate custom data transformation steps—from reformatting complex tables to performing statistical analysis and generating API-ready JSON bodies structures. This step dramatically lowers the barrier to entry for automation: If you can describe your intent in words, this step translates your intent to a production-grade step. Use case title Description Persona Goal Business impact Reconcile shipments across systems using PO-level logic Fills in missing customer data using logic based on PO numbers, enabling root cause analysis across shipment sources Operations Supply chain Enable error investigation and reconciliation Reduce cost from fulfillment errors and customer support escalations Custom cost allocation logic Applies logic to allocate overhead or shipping costs by rule (e.g. % of volume, % of weight) across orders Accounting Improve landed cost and COGS accuracy Increase accuracy of profitability reporting and visibility into landed cost components; support financial audits Summarize historical sales for forecasting Aggregates sales order data by quarter and year to support forecasting and planning workflows Finance Supply chain Improve forecast accuracy Enable more accurate inventory and revenue planning — preventing stockouts and improved cash flow Analyze shipping destinations and costs Identifies top destinations and average shipment weights to inform shipping rate negotiations and regional strategies Supply chain Improve visibility into shipping costs across regions and carriers Reduce shipping spend and improve carrier strategy Calculate SKU-specific lead times Performs complex date logic by SKU to create reference tables used in production and planning flows Supply chain Procurement Create accurate production timelines and ensure POs are being placed in time Prevent stockouts while improving cash flow management Calculate margin by SKU or channel Calculates gross margin across SKUs, channels, or vendors using cost, discount, and pricing data Finance Identify most and least profitable SKUs or channels Improve product mix and pricing strategy to increase margin Pivot-style table transformations Reconstructs large datasets into summaries (e.g. cost per unit by vendor, volume by month, OTIF by warehouse) Operations Enable performance benchmarking by dimension Optimize vendor performance, fulfillment SLAs, and planning Generate API-ready JSON or HTML payloads Converts row-level data into structured formats like JSON or HTML for API integrations Operations IT Enable operators to integrate systems with reduced reliance on IT Accelerate system integrations; reduce cost of IT projects *To learn more about what's possible with Parabola's***Custom transform***step, check out this overview: * ## Categorize with AI The [Categorize with AI](https://parabola.io/product/transform/categorize-with-ai) step helps teams group, label, and tag operational data to uncover root causes, enable strategic reporting, and automate routing decisions. Whether analyzing spend, classifying customer feedback, or routing documents, categorization enables clean structure and insight at scale. Use case title Description Persona Goal Business impact Spend categorization across invoice line items After parsing invoice line items from PDFs, assign them into spend categories such as duties and tariffs, warehousing, transportation, etc. Finance Procurement Supply chain Enable clean, granular spend analysis across vendors Improve visibility into spend composition across vendors to uncover strategic cost-cutting initiatives Customer ticket categorization Analyze tickets across systems like Zendesk and Gladly and classify them based on the content of the email (damaged item, delay, etc.) Customer support Supply chain Identify patterns across your negative customer experiences, and tie those tickets to the 3PL or carrier that handled the order. Improve CSAT while reducing return rates — ultimately boosting profitability. Document type classification (e.g., invoice vs BOL) Classify documents received via emails into types based on document contents (e.g., invoice, CIPL, ASN, BOL) Supply chain Automate document triage and routing to update relevant systems and notify teammates Improve SLA compliance for document processing and reduce manual review time Inbound product categorization Categorizes new SKUs or product lists into internal categories during onboarding or merchandising Operations Maintain clean product taxonomy Improve category-level margin tracking and promotional planning ## Extract with AI The [Extract with AI](https://parabola.io/product/transform/extract-with-ai) step helps teams pull structured data from messy sources like PDFs, support tickets, and emails. This step excels when working with large bodies of raw text, such as email bodies or paragraphs of text. Use case title Description Persona Goal Business impact Address parsing and enrichment Clean and enrich address data received from customers, carriers, and third-parties Supply chain Operations Improve data quality by enriching partial addresses with complete address details Prevent delays stemming from address-issues — resulting in improved SLA performance Pull return reasons from warehouse notes Extract structured return reasons from freeform warehouse notes or logs Operations Identify and address common return causes Reduce return rates Extract order IDs from support tickets Parse Zendesk or Gladly tickets to extract order numbers, products ordered, and other identifiers from unstructured text Customer support Get a full picture of your returns by linking customer tickets to the original order, 3PL, and carrier Improve visibility into return reasons to reduce return rates and improve SLA performance ## Experiment with AI The [Experiment with AI](https://parabola.io/product/transform/experiment-with-ai) step is a **beta step** that helps teams generate summaries, notifications, and contextual content from structured data. Whether it’s summarizing return reasons, generating Slack alerts, or enriching CRM records, this step enables operators to turn data into action faster. Use case title Description Persona Goal Business impact Create Slack alerts based on fulfillment issues Generate proactive messages to post in Slack when data shows an order or fulfillment discrepancy, or is about to breach SLA Supply chain Operations Drive real-time operational awareness Reduce SLA violations and improve time-to-resolution Generate product descriptions from attributes For high-SKU-count businesses frequently receiving new inventory from vendors, auto-populate descriptions to push directly to your ERP/OMS Operations Supply chain Accelerate product onboarding and listing creation Improve conversion rates on product pages and scale product catalog Generate summaries of return reasons Converts raw return data or notes into concise summaries for reporting or alerts Operations Provide actionable summaries to cross-functional teams Improve issue resolution speed and reduce repeated return causes Write CRM company descriptions from domain Enriches CRM records by generating a short company blurb based on the domain or name Customer support Improve the completeness of your CRM Improved data quality enabling better CRM reporting ## How to deploy AI across your team's operations After reading this, you might be thinking it's time to incorporate AI into more of your own team's processes—but where do you start? I'd recommend checking out [Parabola University](https://parabola.io/docs/parabola-university), and specifically the lesson on transforming data with AI: Finally, to see these steps in action—and explore real examples from teams automating complex workflows—check out [Parabola’s Use Case Library](https://parabola.io/use-cases). Happy building! --- # How to Conduct a Freight and Parcel Audit Source: https://parabola.io/blog/how-to-conduct-a-freight-and-parcel-audit If you work in an office, you probably spend some time each week thinking about how you get there—and[how much it costs](https://commutesolutions.com/commute-cost-calculator/). Transit, driving, biking, rideshare: Each method has obvious expenses, but you might not know exact figures without doing a little digging. Maybe you add things up at the end of the month and realize your occasional Uber is[actually a weekly habit](https://www.newsweek.com/woman-spending-100-ubers-daily-1912166) (and you really need to remember to move your car for[street sweeping](https://www.spotangels.com/blog/sf-parking-tickets-most-ticketed-areas-in-sf/)). A company moving goods around is dealing with much bigger numbers, but unless brands are auditing their freight spend, they might be similarly in the dark. Unwanted charges[add up fast](https://ziplinelogistics.com/blog/top-20-accessorial-charges/), and without regular freight audits, it’s difficult to identify needless spend, or remedy the pain points that lead to recurring accessorial fees. Freight and parcel audits are crucial for businesses for whom shipping merchandise is a core business function. By regularly reviewing your shipping invoices and processes, you can identify areas for cost savings, improve efficiency, and ensure you’re not overpaying for services. In this guide, we’ll walk through the steps to conduct a thorough[freight and parcel audit](https://www.youtube.com/watch?v=8gOpklp2dpE). [Flying blind on freight spend? Let Parabola shed some light.](https://parabola.io/demo) ## Gather your shipping data The first step is to collect all relevant shipping data. The bulk of these documents will be carrier invoices (e.g. FedEx, UPS, USPS, DHL) and shipping manifests. Pay particular attention to details on any accessorial fees or surcharges, and be sure that you’re accurately capturing and categorizing these sneaky charges.You’ll also want to compile information on any lost or damaged shipments. This, plus carrier insurance agreements, will give a picture of reimbursements that are owed, and ensure that these funds are being collected as owed, in a timely fashion. There’s one component of shipping data some might not think of: records of any shipping contract negotiations or changes. You want to be sure that you’re aware of the most up-to-date carrier agreements, and that these agreements are being honored across the board. Compiling all of this information will give you a comprehensive view of your shipping spend and history. ## Analyze your data Once you’ve compiled all your shipping data, it’s time to dive into the analysis. First, look closely for any incorrect or duplicate charges on your carrier invoices. These erroneous fees can add up quickly and are often easy to overlook without a thorough review. Another key area to examine is accessorial fees. Many of these surcharges, such as fuel, residential, or oversized package fees, can potentially be negotiated with your carriers or avoided altogether through process improvements. Identifying opportunities to reduce or eliminate these accessorial fees can lead to significant cost savings. You’ll also want to scrutinize how your shipments are being classified and rated. Incorrect classification can result in you paying more than necessary, so validate that your packages are being charged at the proper dimensional weight, service level, and other relevant factors. Digging into these areas of potential overspend will be crucial to optimizing your freight and parcel management. ## Identify opportunities for improvement Based on your analysis, the next step is to identify specific opportunities to improve your freight and parcel management. One key area to explore is your carrier contracts: See if you can renegotiate rates, service levels, or other terms to get more favorable pricing. You may also want to research alternative carriers or shipping methods that could provide cost savings. Another important consideration is your internal processes and infrastructure. Implementing better tracking and reporting systems can enhance visibility into your shipping activities and costs. Optimizing your packaging and fulfillment workflows can also lead to reduced dimensional weight charges from carriers. Additionally, automating invoice auditing and payment can help catch errors and streamline your administrative overhead. Only by digging deeply into your data, and the intricacies of your workflows, can you develop a comprehensive plan to make process improvements. Greater logistical efficiency is a good unto itself, but it’s also guaranteed to bring cost savings—and perhaps even greater revenue. ## Take action and monitor progress Once you’ve identified opportunities for improvement, it’s time to take action. Data is a valuable companion, but it’s an even better friend when paired with good communication. With new insights and a fresh vision, you can work with your shipping providers, logistics team, and other stakeholders to implement desired changes. Note: A freight and parcel audit is not a one-time deal. You’ll want to make this a habit, in order to ensure that process improvements are bringing desired results, and to identify new issues that might bubble up. Continue to monitor your progress, and your freight and parcel management will keep improving. Conducting a thorough freight and parcel audit can yield significant cost savings and operational improvements for your business. By following the steps above, you can gain visibility into your shipping spend, identify areas for optimization, and implement changes to streamline your logistics. --- # How 7 Operators Use Custom Data Transforms to Automate Complex Processes Source: https://parabola.io/blog/how-7-operators-use-custom-data-transformations-to-automate-their-most-complex-processes When asked about Parabola’s newest AI feature, Janie and Jack’s Director of Transportation & Operations, Samantha Mandel, answered in four simple words: “I LOVE THIS STEP.” Based on dozens of submissions from other customers, Samantha’s not alone. We launched the **Custom Transform Contest** to celebrate Parabola’s new [Custom transform step](https://parabola.io/product/transform/custom-transform)—an AI feature that allows you to turn plain language instructions into custom data transformation steps. In the words of our CEO, “it’s honestly magic.” The rules of the contest were simple—submissions were evaluated based on how creatively users could solve real, complex data challenges using the new step. For the winner? $250 in cold, hard cash. For the runners up? Brand new Parabola swag. …and bragging rights for all. Through these submissions, we learned about operators cutting out 50+ steps from their workflows, creating net-new visibility into ERP data, and improving SLA compliance. With so many incredible submissions, we struggled to pick just two—so without further ado, meet your winners: ‍**First place** - [Chad Coley](https://www.linkedin.com/in/chad-coley-9b253621/): Inventory Manager at Harry’s Use case: ERP-to-DC reconciliation **Runners up** - [Dominic Seminara](https://www.linkedin.com/in/dominic-seminara-562259131/): Ecommerce Manager at Cold Cuts Prints Use case: Shipping zone & weight analysis Brian Zhang: Strategic Finance at Anine Bing - Use case: Forecast vs. actual sales summary Deven Hidalgo: Manager, Operations Strategy at Maiden Home - Use case: SKU extraction and lead time calculation Austin Schebaum: Associate Program Manager at Uber Freight - Use case: SLA performance evaluation Samantha Shackett Director of Transportation & Operations at Janie and Jack - Use case: Excel cleanup & enrichment Richard Tran: Business Intelligence Manager at Daylight Transport - Use case: Fuel transaction timestamp normalization ## Before state Before we get into submission details, let’s talk about how operators were solving these problems in the pre-Custom-transform era. The story is almost identical for every respondent—before this step, their use case was *possible* in Parabola, but possible ≠ simple and straightforward. Operators were using anywhere from 2 to 50 additional steps to perform the same data transformation that they’re now achieving in one step—making Flows more complex to understand, more difficult to build, and harder to maintain over time. Let’s see what the new world looks like with the Custom transform step. ## Meet the winner #### Chad Coley: Inventory Manager at Harry’s ##### About the brand Harry’s is a men’s grooming brand that pioneered the D2C model—offering high quality shave, body, hair, and skin care products for reasonable prices. ##### Use case ERP-to-DC reconciliation ##### The challenge Chad needed to reconcile transactions between his company’s ERP system and the distribution center’s transaction report—highlighting any discrepancies across shipments, receipts, or adjustments. Making the task even more difficult, Chad’s data wasn’t standardized across sources, so he needed to join and clean the data before performing the reconciliation. ##### The solution Using the**Custom transform** step, Chad consolidated logic that previously spanned many steps into a single prompt. The step creates new columns, sets new column values based on complex conditional logic, and removes data they no longer need. In Chad’s words, “This approach effectively created an XLOOKUP-like function within the ‘Customer’ column by referencing previous entries for the same PO number. It preserved non-blank entries and, for blank entries, filled in the MAX value for that PO in the column.” > [This Flow] has been invaluable for investigating variances…I’ve found this new step to be extremely helpful in reducing the number of steps required to solve more complex data problems. It’s great at removing the friction of overly complicated logic and gets to the outcome faster.” ##### Business value This step allows Harry’s to uncover variances in their ERP faster and more consistently, improving the data quality in their ERP and ultimately supporting SLA compliance. Additionally, by reducing the complexity in his Flow, Chad made variance reporting workflow easier for the broader team to manage and improve over time. ## Runners up #### Brian Zhang: Strategic Finance at Anine Bing ##### About Anine Bing Anine Bing is a Los Angeles–based fashion brand focused on blending elements of Scandinavian style with American fashion, found in 90+ countries around the world. ##### Use case Forecast vs. actual sales summary ##### The challenge Brian needed to consolidate actual historical sales data and reformat it to align with their forecasting template. This required grouping data by quarter and year, calculating key metrics, and matching the structure used by the finance and planning teams. ##### The solution With the **Custom transform** step, Brian generated a dynamic summary following his team’s internal template. The step grouped sales data by quarter and year, aggregated relevant metrics, and output a dataset formatted to match the forecasting model. ##### Business value After proving out this forecasting approach in Parabola with one step, Brian can now roll out similar forecasts to other parts of the business to support planning activities. #### Deven Hidalgo: Manager, Operations Strategy at Maiden Home ##### About Maiden Home Maiden Home is a modern luxury furniture brand that delivers handcrafted, made-to-order pieces directly to customers. ##### Use case SKU extraction and lead time calculation ##### The challenge Deven was facing a classic operator challenge: She needed to join unstandardized SKU data from multiple sources so that she could calculate lead times and communicate results to her business partners. This involved tons of complex conditional logic and data extraction rules to clean and join her datasets. ##### The solution By using 14 **Custom transform** steps in her Flow, Deven was able to cut the complexity from her old Flow and replace it with atomic steps that simply perform each of these functions, simply described in plain language. > I love that this feature saved me so many steps and let me re-use logic in a much cleaner way.” ##### Business value At the end of this Flow, Deven creates a master lookup table that’s leveraged by the business at large. Using this data, Deven and Maiden’s business partners can stay on top of real-time cost, lead time, and inventory data. #### Austin Schebaum: Associate Program Manager at Uber Freight ##### About Uber Freight Uber Freight empowers shippers with a comprehensive suite of logistics solutions, combining advanced technology with an extensive carrier network to optimize every step of the freight lifecycle. ##### Use case SLA performance evaluation ##### The challenge While comparing reported appointment dates against the latest required delivery date, Austin needed to perform a date comparison and reformat data values in his Flow. By executing this comparison, he could proactively alert the appointment scheduling team in real time about any shipments that may require manual intervention. > The step has been great! Enjoyed seeing what it is capable of.” ##### The solution Using the **Custom transform** step, Austin wrote a single prompt to compare delivery and expected dates, apply threshold logic, and flag records for human intervention. The step condensed everything into one place, simplifying the build and making the SLA logic reusable across other Flows. ##### Business value Uber Freight is already an [automation powerhouse](https://parabola.io/customers/uber-freight). With this step, they’re adding to their collection of re-usable elements, making it easier to continue scaling up their automations across the business. #### Samantha Schackett: Director of Transportation & Operations at Janie and Jack ##### About Janie and Jack Janie and Jack is a modern children’s apparel brand offering elevated, timeless fashion for kids. ##### Use case Multi-column Excel cleanup & enrichment ##### The challenge After converting a partner’s .txt file to an Excel file, Samantha needed to clean and enrich a dataset full of blank values. These cells needed to be filled based on prior row values and required transformations across three separate columns—including updating a date, changing a value, and inserting a symbol—all based on conditional logic. ##### Previous approach Coming from the world of Excel, Samantha knew the exact function to use for this transformation in Excel—but as a brand new Parabola user, she was still learning about the best step for each transformation. > I LOVE THIS STEP. Will definitely be using this in the future!” ##### The solution Samantha was able to creatively leverage her knowledge of Excel to build an effective solution in Parabola. In her **Custom transform**step, Samantha provided the formula that she *knew* would work in Excel: =IF(A2="",A1,A2)Since Parabola’s step can handle just about any type of instructions, the step knew exactly what Samantha was trying to accomplish based on her Excel description—which perfectly filled in her columns. ##### Business value This allows Samantha’s team to handle messy vendor files and transform data from external systems more efficiently. Ultimately, this enables faster ingestion, reduced manual error, and streamlined reporting handoffs to downstream stakeholders in transportation and logistics. #### Richard Tran: Business Intelligence Manager at Daylight Transport ##### About Daylight Transport Daylight Transport is a leading expedited less-than-truckload (LTL) carrier in the United States, known for its high-speed, reliable freight services. ##### Use case Fuel transaction timestamp normalization ##### The challenge Richard needed to process fuel transaction data from a vendor who delivered the date and time in two separate columns. To prepare the data for ingestion into a third-party platform (Samsara), he had to combine the date and time, convert it to military time format, and apply a time zone adjustment. ##### The solution With **Custom transform**, Richard condensed all 16 steps into a single prompt. The transformation logic merged and reformatted the data in one go, drastically simplifying the Flow and making it far easier to modify and replicate across similar data pipelines. > I will definitely implement this new step in a lot more flows!” ##### Business value This transformation simplifies Flow maintenance and ensures timestamp formatting is standardized before pushing data to critical external systems like Samsara—boosting data accuracy and reducing friction in the integration. #### Dominic Seminara: Ecommerce Manager at Cold Cuts Prints ##### About Cold Cuts Prints Cold Cuts Prints is a fast-growing ecommerce brand that specializes in high-quality custom art prints. ##### Use case Shipping zone & weight analysis ##### The challenge Dominic was tasked with analyzing which U.S. states and countries Cold Cuts Prints ships to most frequently, along with the average weight per shipment. Using this data, he could help ensure the team was always obtaining competitive shipping costs, which is much easier said than done when working with messy carrier data. > This was possible to do without **Custom transform**, but it would’ve taken 50+ steps. Now it’s fairly simple—and impressive.” ##### The solution With a single **Custom transform** step, Dominic can now group shipments by state and country; calculate average weights; and get clean, actionable insights as an output. What once required 50+ steps was now condensed into one, making the Flow dramatically simpler and faster to iterate on. ##### Business value This transformation enables Cold Cuts Prints to continuously monitor their shipping patterns to improve cost efficiency and, in their words, discover new market opportunities. It empowers the team to make strategic decisions around fulfillment and regional marketing, using operational data in smarter ways. *—* To learn more about additional use cases for Parabola’s **Custom transform** step and explore prompting best practices, check out our [launch announcement doc](https://parabola.io/product/transform/custom-transform). Congrats to our winners and happy building! --- # A Greener Disposition: Tips for Tackling Excess Inventory Source: https://parabola.io/blog/a-greener-disposition-tips-for-tackling-excess-inventory Figures vary by industry, but excess inventory is produced in essentially every commercial sector. Within the global fashion marketplace, an estimated [30% of production](https://www.the-spin-off.com/news/stories/The-Trends-Finding-solutions-for-unsold-stocks-Its-the-next-challenge-18262#:~:text=The%20fashion%20market%20is%20said,up%20to%2040%2D50%25.) goes unsold; that number approaches 38% in the U.S. food system, where [80 million tons](https://refed.org/food-waste/the-problem/#:~:text=In%20the%20U.S.%2C%2038%25%20of,half%20by%202025%20or%202030.) of surplus food become food waste. In total, the value of these unsold goods in the U.S. is [thought to exceed $740B](https://www.mckinsey.com/industries/retail/our-insights/thinking-beyond-markdowns-to-tackle-retails-inventory-glut).The environmental impacts are hefty, and sustainability is a major driver towards improved disposition, the process of donating or liquidating excess inventory. But according to Charles Cushing, founder of [StartOps](https://startops.network/), there’s “more than one bottom line.” With countries in the EU [levying regulatory pressure](https://www.sciencedirect.com/science/article/pii/S2352550922003050#:~:text=In%20parallel%2C%20consumer%20expectations%20regarding,such%20as%20resale%20or%20redistribution.) to prevent the destruction of unsold goods, or to incentivize the sale of donated merchandise, the monetary incentives are clear. Notably, even before growing StartOps from an operations community into an [ops “USEletter”](https://www.linkedin.com/posts/charlescushing_exactly-1-year-ago-i-launched-a-weekly-activity-7267902413636878336-aPfy?utm_source=social_share_sheet&utm_medium=member_desktop_web) and vendor database, Cushing had firsthand experience with liquidation and resale. Keep reading to discover some of the reasons why disposition is needed, what vectors influence choosing a disposition partner, and what services might be a match for your business needs. [Unexplained inventory discrepancies? Parabola can help.](https://parabola.io/use-cases/inventory-reconciliation) ## Inside the secondhand market Before building StartOps, Cushing got his hands dirty as COO of Twice, a clothing reseller that was acquired by eBay in 2015. Twice had an interesting role as both a disposition service for its seller-customers, as well as a user of those services itself. Twice’s model worked by providing free shipping labels to its users, whose clothing would be sent to Twice for grading. Shipments ran the gamut, from like-new goods, to pieces that “looked like a dog ate them.” Those unsellable items had to go somewhere, and Twice started out with weekly pickups from Goodwill. Soon, they were carting off a packed truck every day, and that pace left little room for error; if even a single trip was missed, clothes would come “spilling out of the warehouse.” Thus began the journey into the “very offline” inventory disposition space. Through some “random Googling,” Cushing stumbled on an inventory liquidator that specialized in apparel. He negotiated a price per pound and scheduled less-than-truckload (LTL) pickups as needed. Beyond increased reliability, the revenue from re-selling cast-offs helped meaningfully improve per-item margins. This partnership lasted through acquisition, which required Twice to sell all excess inventory, and finally send off its last load. ## Navigating the liquidation landscape Cushing’s search for a disposition provider brought him into a no-man's-land, and a dozen years later, the space is still much less developed than the commerce companies that find themselves wading into it. Selling excess inventory is a historically low-tech endeavor, and that can come with a lack of visibility into the disposition process. New startups are bringing transparency, but it can still be challenging to be sure you’re matching with the right service. As with many partnership considerations, Cushing advises returning to core principles wherever possible. First, there are basic supply chain questions: What is the actual good being disposed of? Clothing and electronics, for example, follow vastly different paths to renewal, refurbishment, resale, or disposal. Seeking out other operators in your space might help you understand what options are available, and even find close matches to your particular use case. [The conversation's happening. Apply to The SOP Community now.](https://parabola.io/resources/the-sop-community) The other major consideration is the vectors influencing your search. Cushing asks: “How much value are we trying to recover? How traceable and sustainable is the process? What is the impact on the brand?” Goodwill might be a great outlet for spring closet cleaning — less so for a company that wants to recoup its costs, or maintain its brand integrity by retaining control over where its excess inventory ends up. ## What are my options? Trusted resources like StartOps are a handy place to look for [disposition providers](https://startops.network/lists/inventory-disposition), but it can still be a helpful exercise to think through the pros and cons of some of the available options. #### General donation [Goodwill](https://www.goodwill.org/donors/donate-stuff/), or a similar local secondhand shop, is an easy place to start. Godwill has outlets across the U.S., and vetting for sale takes place only after donation, so there are few barriers to disposal. Your Goodwill donation receipt can translate into a quick tax writeoff, but there’s no transparency after that initial contact, and Goodwill’s logistics network is less optimized for the volume required by enterprise users. [Good360](https://good360.org/#:~:text=Why%20Good360?,we%20all%20need%20to%20thrive.) is a more operationalized alternative: The organization matches nonprofits in need with donations from retail juggernauts like Amazon, Nike, and Walmart. Good360 earns high marks for sustainability — they source donations carefully, and match them with local recipients to reduce emissions — but since donations are driven by urgent needs, those needs might not match up with your business’s excess inventory. #### Tech re-use and recycling With a secure chain of custody, and full visibility into the afterlife of your e-waste, [Happen Ventures](https://happenventures.com/solutions/electronic-waste-recycling/) prioritizes putting you at ease as you hand off your most sensitive materials. Happen Ventures also has a re-use first ethos, and aims to rehome donations wherever possible, before turning to recycling. Again: points for sustainability, but don’t expect to recover any value beyond a writeoff. #### Apparel renewal and liquidation Apparel resale is a relatively robust vertical within the inventory disposition space, but startups like [(re)vive](https://www.byrevive.com/how-it-works) are still innovating. (re)vive works with brands to determine resale standards, then sorts returns, deadstock, and damaged goods for refurbishment or disposal. They use a unique multichannel process: return some refurbished goods to brands for sale as-new; pass the remaining refurbished items to its marketplace of influencer resellers; and dispose of only the merchandise that can’t be revived. This model gives partners greater ability to maintain brand integrity, while keeping an eye on sustainability, and even recovering value. #### Resale marketplace Another startup with a similar model is [Ghost](https://www.ghst.io/), which takes merchandise across durable goods categories, and resells them on a discreet marketplace. While (re)vive touts the benefits of its marketplace in increasing brand awareness, Ghost takes the opposite route: only its buyers can see which brands use the platform, and sellers have total control as to channels, retailers, and localities of resale. Word has it they’re selective about the sellers they accept, so it’s not a last-minute solution, but this is the best option for a business that wants to maintain total control over brand integrity. ## The future of excess inventory Sustainability concerns don’t always translate into changes in consumer habits — but there’s evidence that companies that tout their environmental or social advantages [grow faster than those that don’t](https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/consumers-care-about-sustainability-and-back-it-up-with-their-wallets). Brands like [H&M](https://www.thesustainablefashionforum.com/pages/hm-is-being-sued-for-misleading-sustainability-marketing-what-does-this-mean-for-the-future-of-greenwashing) and [Brandy Melville](https://goodonyou.eco/how-ethical-is-brandy-melville/) have come under fire in recent years for their disposition practices, and it’s clear that consumers at least *want* to buy more responsibly. Remember the adage that there’s more than one bottom line; sustainability efforts have to create wins for the consumer too, or they won’t take root. Cushing highlights [Dispatch Goods](https://dispatchgoods.com/), which partners with meal kit services to create a sustainable circular system for reusing food packaging. The logistics are simple for the consumer, and the packaging itself is attractive: slim, durable, and made of shiny stainless steel. Dispatch Goods estimates these containers last for 1,000 uses, at a fraction of the price of so much disposable packaging, loosening margins and passing savings to the consumer. Whatever the solution you choose, a disposition partner isn’t a magic bullet: Sustainability starts inside the house. Even before excess inventory becomes a concern, you can get better visibility into your inventory with software like Parabola, which quickly and automatically syncs data across many inventory tracking platforms. And when you decide it’s time to clean up, Parabola can help you build that new SOP too. --- # How to Automate 3PL Scorecarding and Crush OTIF Goals Source: https://parabola.io/blog/how-to-automate-3pl-scorecarding-and-crush-otif-goals If you’ve ever ordered multiple items from Amazon, there’s a good chance you’ve had to select a [different delivery date](https://www.shipbob.com/blog/split-shipments/) for each item. It’s a simple interface, but each of those date selections kicks off intricate logistical chains, through the vast network of Amazon fulfillment centers, and across countless third-party sellers. The same thing happens on different scales for all consumer brands, and that amount of data presents an immediate challenge: How can you tell if packages are arriving on time? For smaller brands looking to get and keep loyal customers, this is a pressing question indeed. Every positive customer experience drives growth and loyalty, so getting orders to the right place, [on time and in full](https://www.fourkites.com/blogs/maximizing-on-time-in-full-otif-in-the-supply-chain/) (OTIF), is crucial. Maintaining high OTIF performance is no easy feat. There are endless factors that can impact delivery, from internal inventory and production challenges to unreliable suppliers and 3PL issues. Even the most carefully planned operations can be derailed by sudden spikes in demand or other external disruptions. To control as many of these uncontrollable variables as possible, leading brands are turning to [automated scorecarding systems](https://parabola.io/processes/what-is-vendor-scorecard-reporting) to closely monitor their 3PL partners' performance. By gathering comprehensive data and transforming it into actionable insights, these brands can identify issues early, drive continuous improvement, and deliver a seamless customer experience. [Cursed with supply chain blindness? See more clearly with Parabola.](https://parabola.io/demo) ## Limited visibility hampers OTIF performance One of the biggest barriers to measuring performance is getting adequate visibility into 3PL operations. "Every 3PL has their own WMS…I have yet to come across one that has robust reporting and visual reporting all in one package," explains one Parabola customer. An existing WMS might provide basic data, but many lack the visualization capabilities needed for effective performance tracking. Additionally, data tends to be scattered across different reports, making it hard to get a complete picture of operations. Orders, inventory data, and track and trace workflows are stored in separate systems, often in different formats. It requires a significant amount of manual work to consolidate and reconcile disparate data types, so timely audits of OTIF benchmarks are nearly impossible. ## Automated vendor scorecarding with Parabola This is what [Parabola is built for](https://parabola.io/processes/vendor-scorecard-reporting-software) — pulling data from every dark corner of your tech stack, and compiling it in the light of day. While manually sourcing disparate data and applying transforms in Excel might be feasible weekly or monthly, keeping daily tabs on OTIF performance is impossible to do by hand without scaling headcount. Parabola can automate that process multiple times hourly, if needed. Here's how a vendor scorecarding Flow might work: - Pull in order fulfillment, status, and shipping data from several endpoints in the vendor's API - Sum up key metrics by channel by day/month/year (how many units were shipped to retailers, through the website, etc.) - Compare to historical trends (i.e. same time last year) as well as budgeted benchmarks - Push into a visualization that shows the aggregate data By consolidating all the performance data into a single, centralized system, scorecarding provides a holistic, real-time view of 3PL operations. Users can easily switch between different time periods, channels, and other dimensions to uncover insights tailored to their needs - without the hassle of piecing together information from multiple reports. ## Improved vendor oversight and performance Implementing and automating a scorecarding system can deliver significant benefits for logistics operations. The classic adage says you can’t change what you don’t measure, and this holds true for vendor scorecarding and the 3PL relationship. Measuring the same data points consistently means variance can be identified quickly, and root cause analysis performed more seamlessly. If there’s a sudden dip in on-time deliveries, for example, you can easily cut through the noisy data to find where a particular warehouse or pick session is to blame. This is particularly useful at the busiest times of year. With comprehensive trend insights at your fingertips, ongoing scorecarding helps proactively identify risks and opportunities — and automating the process means logistics teams can focus on making data-driven decisions, rather than drowning in spreadsheets, or simply flying blind when orders are surging. But perhaps the most important gift of vendor scorecarding is accountability. Poor visibility goes both ways, and if a brand can’t show its work by setting concrete performance indicators and goals for improvement, the relationship will suffer from a lack of trust. For brands looking to improve their vendor oversight, this is the power of automated scorecarding: to transform murky data into the foundation of a dream logistics partnership. --- # What Is OTIF in the Supply Chain? Source: https://parabola.io/blog/what-is-otif-in-the-supply-chain Aren’t there enough acronyms on this spinning marble? No! There’s always room for one more — come on in; the fire’s crackling. On-time and in-full (OTIF) delivery is a critical metric for [measuring the performance](https://parabola.io/use-cases/vendor-scorecard-reporting) of your supply chain. OTIF tracks whether your products are reaching customers when promised, and in the correct quantities. It's a key indicator of how well your logistics operations are running. OTIF is especially important for consumer brands that are committed to delivering a seamless customer experience. Every positive [order fulfillment builds loyalty](https://parabola.io/processes/what-is-order-fulfillment), while late or incomplete deliveries can erode trust and drive customers away. [Can’t keep your acronyms straight? Parabola brings order to the chaos.](https://parabola.io/demo) ## What is OTIF? OTIF stands for "on-time and in-full" delivery. It measures two key aspects of your supply chain: **On-time**: The percentage of orders that are delivered to the customer by the promised date. This requires carefully coordinating production, inventory, and transportation to ensure products arrive when expected. **In-full**: The percentage of orders that are delivered with the full quantity the customer ordered. Maintaining high in-full rates means minimizing stockouts, back-orders, and other fulfillment issues. Together, on-time and in-full delivery creates an [optimal customer experience](https://www.adaptideations.com/otif-in-the-supply-chain-what-is-it-and-how-to-calculate-it). When an order is both on-time and complete, it builds trust, satisfaction, and the likelihood of repeat business. Tracking OTIF provides valuable insights into the health and efficiency of your supply chain. By closely monitoring these metrics, you can identify problem areas, make strategic improvements, and ultimately exceed customer expectations. ## Why is OTIF important? OTIF is a crucial performance indicator for several reasons: 1. **Customer satisfaction**: On-time and in-full deliveries are key to meeting customer expectations and maintaining loyalty. Anything less can lead to negative reviews, returns, and churn. 1. **Operational efficiency**: OTIF reflects how well your supply chain is functioning. By identifying issues that impact on-time or in-full performance, you can optimize processes and reduce costly errors. 1. **Competitive advantage**: In today's fast-paced consumer landscape, reliable order fulfillment is a major differentiator. Brands that consistently deliver on OTIF stand out from the competition. 1. **Revenue growth**: Positive customer experiences drive repeat business and referrals. Maintaining high OTIF rates is essential for sustainable revenue growth, especially for e-commerce and direct-to-consumer brands. ## How to improve OTIF Improving OTIF performance requires a holistic view of your supply chain operations. The most important step in improving OTIF numbers is to measure them accurately. Collect comprehensive data on your order fulfillment process, from inventory levels to carrier performance. Automated tools can help consolidate this information for easy tracking and analysis. Armed with accurate, up-to-date data, brands can begin to identify challenges and set goals. Use your OTIF data to pinpoint the root causes of delivery issues, whether it's production delays, shipping errors, or a secret third option. With these root causes in mind, you can establish realistic OTIF goals for your business, considering both historical trends and industry benchmarks. After goal-setting, it’s time to make process changes. Implement lean manufacturing principles, streamline workflows, and leverage technology to enhance efficiency and resilience across your supply chain. Collaborate closely with 3PLs, suppliers, and other vendors to align on OTIF priorities and hold each other accountable. ## Automating OTIF tracking with Parabola One of the biggest challenges in measuring OTIF performance is getting comprehensive visibility into your supply chain data. Order, inventory, and shipping information are often scattered across multiple systems, making it difficult to consolidate and analyze. This is where automated tools like Parabola can [make a big difference](https://parabola.io/use-cases). Parabola is designed to pull data from every corner of your tech stack, transform it into actionable insights, and deliver real-time reporting on key metrics like OTIF. With Parabola, you can build a vendor scorecarding workflow that pulls in order fulfillment, status, and shipping data from your 3PL's API; calculates on-time and in-full delivery rates by channel, over time; compares performance to historical benchmarks and target goals; and visualizes the results in an easy-to-understand dashboard By consolidating all your OTIF data in one centralized system, you can quickly identify issues, uncover trends, and make data-driven decisions to improve your supply chain operations. Automating this process frees up your logistics team to focus on strategic initiatives rather than manual reporting. Ultimately, the power of Parabola for OTIF tracking is about transforming murky data into clear, actionable insights. With comprehensive visibility and the ability to quickly analyze performance, you can turn your supply chain into a competitive advantage and deliver an exceptional customer experience. --- # Best Warehouse Management Softwares (WMSs) In 2025 and Beyond Source: https://parabola.io/blog/the-best-warehouse-management-systems-for-scaling-operations If you’re not scarred by a drawn-out implementation of an ERP that rhymes with “jet fleet,” you might be curious what software other teams use to keep their supply chains running. We definitely were — that’s why we conducted a survey to ask operators across industries and verticals, from budding ecommerce brands to billion-dollar companies, what tools grease the gears (and keep their customers happy). From returns management to order fraud protection, it can be overwhelming to evaluate tools, and you might find yourself wondering: Are these even necessary? Across the board, we found that companies are DIY-ing many of these tools — either building in-house, or circumventing them entirely. But one function is so important that more than 4 in 5 brands use software for it: warehouse management. Warehouse management is a critical component of any successful ecommerce or logistics operation. Choosing the right[warehouse management software](https://parabola.io/blog/the-best-warehouse-management-systems-for-scaling-operations) (WMS) can make the difference between streamlined, efficient inventory management and fulfillment processes and ones plagued by bottlenecks and costly mistakes. In this comprehensive guide, we'll explore the most cited warehouse management[software solutions](https://parabola.io/the-supply-chain-tech-stack-report-lp) from our survey, analyzing their features, benefits, and suitability for businesses of different sizes and industries. Whether you're a small startup or a large enterprise, you'll find the information you need to make an informed decision and optimize your warehouse operations. [Ready to automate inventory workflows? Give Parabola a try.](https://parabola.io/demo) ## Why does a WMS matter? Effective warehouse management is essential for businesses that store, process, and ship physical goods. A robust WMS can provide a range of benefits, including improved inventory control and visibility; reduced labor and storage costs; and greater customer loyalty, stemming from accurate, timely order fulfillment. With the right WMS in place, businesses can unlock significant cost savings, improve their competitive edge, and better serve their customers. ## Key features to look for in your warehouse management system As with many business management tools, WMS software offers lots of bells and whistles. To ensure you choose the right WMS for your business, keep an eye out for the following features: - **Inventory management**: Accurate real-time tracking of stock levels, item locations, and product movements. - **Order fulfillment**: Streamlined workflows for receiving, picking, packing, and shipping orders. - **Warehouse optimization**: Tools for maximizing space utilization, optimizing workflows, and reducing labor costs. - **Reporting and analytics**: Comprehensive data insights to support informed decision-making and continuous improvement. - **Integrations**: Seamless connectivity with your existing ecommerce platforms, shipping carriers, and other business systems. By prioritizing these features, you can make sure you’re signing up with a WMS that can tell you how you’re doing, and help you get better. ## Top warehouse management software solutions When it comes to warehouse management software, there is no one-size-fits-all solution. The best system for your business will depend on a variety of factors, including your industry, the size and complexity of your operations, and your specific needs and goals. Here are some of the most popular WMS platforms according to our survey, ordered roughly by ideal client size or level of service offered: #### [ShipBob](https://www.shipbob.com/) **Overview**: ShipBob provides a fully integrated WMS with fulfillment-as-a-service, making it a great choice for direct-to-consumer (DTC) brands that want to outsource their warehousing and shipping. With ShipBob, businesses can store inventory in multiple fulfillment centers across the U.S. and abroad, improving delivery speed and efficiency. - **Strengths**: ShipBob offers quick setup and easy-to-use technology, without the need to manage in-house warehouses. The system is especially suited for DTC brands looking to scale their fulfillment operations quickly without substantial upfront investment in infrastructure. - **Weaknesses**: While ShipBob offers convenience, businesses may have less control over fulfillment processes and customer experience, especially in terms of inventory management and order accuracy. - **Best for**: Small-to-medium DTC brands that want to offload fulfillment responsibilities while maintaining control over inventory and shipping. #### [ShipHero](https://shiphero.com/) **Overview**: ShipHero offers a cloud-based WMS designed specifically for e-commerce and direct-to-consumer (DTC) brands. It provides real-time inventory tracking, order automation, and warehouse optimization, making it ideal for growing businesses looking to streamline fulfillment. - **Strengths**: ShipHero is easy to use, integrates seamlessly with e-commerce platforms like Shopify and Amazon, and provides robust automation for order picking and shipping. - **Weaknesses**: While it excels in e-commerce fulfillment, larger enterprise businesses may find its features limited compared to full-scale ERP-integrated WMS solutions. - **Best for**: Mid-sized e-commerce brands looking for a fast, efficient, and affordable WMS that integrates seamlessly with online sales platforms. #### [Extensiv](https://www.extensiv.com/) **Overview:** Extensiv WMS is a cloud-based warehouse management system designed for 3PLs and growing e-commerce businesses. It provides real-time inventory tracking, order automation, and warehouse optimization, making it ideal for businesses looking to scale fulfillment operations efficiently. - **Strengths:** Extensiv WMS offers robust multi-client management, seamless integration with major e-commerce platforms and ERPs, and powerful automation tools for inventory and order processing. - **Weaknesses:** While highly capable for 3PLs and mid-sized businesses, larger enterprises with complex supply chains may require additional customization compared to full-scale ERP-integrated WMS solutions. - **Best for:** 3PLs and mid-sized e-commerce brands seeking an efficient, scalable, and automation-driven WMS that integrates seamlessly with multiple sales and fulfillment channels. #### [Manhattan Associates](https://www.manh.com/) **Overview**: Manhattan WMS is a comprehensive, enterprise-level solution for large-scale warehouse operations. It offers advanced features such as AI-driven automation, dynamic slotting, and labor management tools that optimize productivity across warehouses. With real-time analytics and predictive intelligence, it can help businesses maintain the highest efficiency in their operations. - **Strengths**: Manhattan WMS provides powerful automation for labor management, warehouse operations, and inventory flow. Its AI-driven insights are designed to minimize human error and ensure faster order fulfillment. - **Weaknesses**: Due to its advanced features, Manhattan WMS can be costly and requires significant implementation time. Smaller businesses may find its complexity overwhelming, as it’s tailored for large enterprises. - **Best for**: Large retailers and enterprises that handle high-volume operations and need robust automation to drive efficiency. #### [Fulfil](https://www.fulfil.io/) **Overview**: Fulfil is a cloud-based ERP designed specifically for high-growth eCommerce brands that need warehouse management capabilities integrated with their entire business operations. Unlike standalone WMS solutions, Fulfil provides a unified platform that combines inventory management, order fulfillment, accounting, and reporting in one system, eliminating the need for multiple disconnected tools. - **Strengths:** Fulfil excels at providing real-time visibility across all channels and warehouses, with native integrations to major eCommerce platforms like Shopify, Amazon, and wholesale channels. Its strength lies in connecting warehouse operations directly with financials, and it offers advanced features like multi-warehouse management and batch tracking. - ‍**Weaknesses:** As a full ERP solution, Fulfil may offer more functionality than businesses looking for just warehouse management need. Companies with very simple operations might find the comprehensive nature more than necessary. - ‍**Best for:** Fast-growing eCommerce brands that need to graduate from disconnected systems to an integrated platform. Ideal for companies managing multiple sales channels and warehouses, or those planning international expansion. #### [Bonus: Parabola](https://www.parabola.io/) Like ShipStation, Parabola is not technically a WMS, but it could just be the missing piece in your supply chain function. Even if you Goldilocks your way to the perfect WMS, you might find you have trouble making that platform talk to your other business management tools. Most brands and 3PLs end up needing a solution that’s as unique as their business — that’s why Parabola was the most popular workflow automation tool in our survey. ## The key to a WMS match: right-sizing Choosing the right warehouse management software is a big decision that can have a significant impact on your business's efficiency, profitability, and customer satisfaction. Even understanding the difference between an enterprise-level WMS like Manhattan Associates and a more affordable tool like ShipHero has you well on your way to finding the right match for your business. Whether you're a small ecommerce startup or a large-scale logistics operation, there's a warehouse management system out there that can help you streamline your processes, reduce costs, and deliver an exceptional customer experience. Carefully evaluate your options — we’re here to help. ‍ --- # Why Ecommerce Brands Need to Get Loud About Order and Returns Fraud — and How to Prevent It Source: https://parabola.io/blog/why-ecommerce-brands-need-to-get-loud-about-order-and-returns-fraud-and-how-to-prevent-it When it comes to fighting order and returns fraud, Theodore Roosevelt’s favorite adage “speak softly and carry a big stick” doesn’t cut it. If brands want to ward off bad actors, they need to get loud and carry the biggest stick they can find. “Once people figure out you're not a soft target and that you’re watching and checking for this, you’ll see a significant reduction in fraud," says Kyle Bertin, CEO and co-founder of return processing software Two Boxes. With fraudsters becoming increasingly sophisticated (and working more collaboratively), the time to get vigilant is now. [The SOP](https://parabola.io/resources/the-sop-community) Community recently brought together a panel of ecommerce experts to talk about the trends they’re seeing and how to be systematic about implementing changes that will protect your business. The panel included: The conversation was rich with actionable insights that you can implement today. Here’s what they had to say. ## Watch the panel recording ## Understanding popular order and returns fraud strategies There are three main types of fraud that Bertin sees with the brands he works with at Two Boxes: wardrobing, empty box fraud, and fake tracking ID (FTID) fraud, which he [benchmarks](https://www.twoboxes.com/returns-playbook) at about 10%, 3%, and 3-5% of all returns, respectively. **Wardrobing** is the classic buy-for-an-event-then-return-used strategy. “Unfortunately, we see this a lot with athletic apparel,” Bertin says, “where stuff comes back and someone clearly wore it to an exercise class and it’s gross and smelly.” Nice. **Empty box fraud** jumps to the next level of deceit. This is when someone returns a box that is empty — or filled with rocks to try and simulate the weight of the product. And lastly, we have **FTID fraud**, which Bertin says is growing at an alarming rate: “When we published our benchmarking report, it was about 1.5 to 2% of the returns that we saw, but we've actually seen it climb more to 3 to 5%. It's growing really fast, and I think it's the most nefarious.” When engaging in FTID fraud, bad actors will: From the customer service team’s point of view, it looks like it was dropped off by the customer because of the limitations in carrier tracking capabilities. From there, the fraudster will reach out to your customer service team and demand a refund. Unfortunately, this is something that Caton has been noticing at Rapha: “We’ve experienced a lot of this, particularly in our UK market, where people go the route of laying the blame with the carrier. They say they sent it back, but it never gets back to the warehouse. Regardless, they can say, ‘Where’s my refund?’” Caton notes the growing sophistication of this kind of behavior: “These people are smart. They don’t place really high orders, they’ll do multiple orders of a relatively low value — products that they know they can probably sell. We see it on reselling platforms all of the time. We can actually see the batch picking order number on the product and trace it all the way back to the customer, but there’s nothing we can do at that point.” The group agreed: Once products have been dispatched, there’s nothing that can be done. ## Preventing order and returns fraud The panel had a number of ideas regarding what to try — and not try — when it comes to taking action against fraud. The first step is making sure you have the data and dashboards in place to inform your strategies and make fraud detection easier. Some things you’ll want to keep an eye on are: names (bad actors often won’t input names); fraudulent addresses; average order value and any orders that fall significantly outside of that range; how many times customers have returned and if you can identify a pattern; and what your product costs. “Know your numbers, know what your AOV is, know what your product costs are as a leader in your company,” Scanlon elaborated on that last data point. “Let’s say one customer service team member is having to spend one hour on one order that looks fraudulent, that very well could be a loss to you depending on your product cost, so it’s important to assess the ROI on the time your team is spending on these reviews.” Once these baselines are set, and you know what is worth pursuing from an ROI perspective, here are some things you can do to be more proactive in your fight against fraud. #### Collaborate with your warehouse or 3PL to develop an SOP for fraud detection A great force for protecting your brand from fraud exists in your warehouse — if you commit to putting a strong [SOP](https://parabola.io/resources/the-sop-community) in place and fostering healthy collaboration. Gilvey has seen this firsthand. “The folks who are packing the orders are such a good line of defense. I have so many examples of times where we caught fraudulent orders because someone was just paying really close attention.” Ultimately the folks packing and processing products are going to be familiar with what normal and abnormal looks like, especially if it’s your brand’s warehouse. Don’t hesitate to lean on them to sniff out suspicious orders and train them to spot what those might look like. If everyone knows the bottom-line impact of returns fraud on the business, it’s easier to mobilize the warehouse, the customer service team, and others to help But if you’re working with a 3PL, a more hard-line approach could be in order: “3PLs don’t necessarily care about the return side of their business,” says Scanlon, “so you have to spend more time being very specific in the SOPs.” What does specific look like? According to Bertin, that could mean detailing exact specifications for different garments around what to look for when they’re returned. Does the zipper still zip? Are there signs of wear? Can the item be rehabilitated if it’s high enough value? Or also knowing when it makes more sense to cut your losses. Another best practice is to order from and return your own products every now and then to check that your processes are being run the way you’ve detailed. If you’ve implemented an SOP, it’s worth QAing that it’s being done to your standards and per your agreement. #### Issue refunds after processing returns — and build a buffer between purchase and fulfillment Return refunds commonly take place when a return is initiated, but Bertin recommends rethinking this if you’re seeing a lot of instances of fraudulent returns. Instead of refunding upon the *initiation* of the return, wait until the return has been processed. This way you can catch empty boxes, goods that have been worn, or other violations of your return policy. “Just try it,” Bertin recommends and see how it goes. More often than not, he finds that it doesn’t affect the customer experience and moves the needle significantly when it comes to catching fraudulent returns. Just be sure to let your customer service team know so they can tag any complaints or feedback that suggests it might not be the right fit for your brand. Additionally, Scanlon recommends adding a buffer between when a customer purchases something and when it gets fulfilled. This doesn’t need to be a significant amount of time, just enough for order fraud detection processes to take place. #### Take advantage of native Shopify fraud alerts If you use Shopify, Scanlon recommends taking advantage of their native fraud detection capabilities. The caveat? Make sure it’s not turning away real customers. “Sometimes the 10-step process that they go through can be too onerous and prevent legitimate customers from making purchases. Because of that, I would recommend having your customer service teams set up ticket tags for this specific instance. So if you see a trend of legitimate customers reaching out and getting blocked you can go back and revisit the process and make sure it’s not too strict,” advises Scanlon. #### A/B test order insurance Lastly, you can always experiment with implementing order insurance at checkout. This is something Gilvey tried in a previous role. She was working with really high AOV orders and was finding that the costs associated with replacing lost orders were really high. Her team investigated a few insurance providers, made a decision, and implemented it. While they found that the actual customer experience of using the insurance was good, it was leading to a drop in conversion. They did some math to determine whether the money they were saving from the insurance claims was comparable to the money lost from the conversion drop. Turns out it wasn’t. “I think the decision for any ecommerce brand is very product specific,” Gilvey says. “It’s taking a look at your cart value, taking a look at your conversion rate, and running a test. I would advise folks to look at it holistically and not just look at the potential savings in this one narrow area.” ## Determine your baseline metrics, and start experimenting At the end of the day, fraudulent activity is becoming increasingly organized and sophisticated, and it’s a major concern that brands should be combatting in every way they can. If you’re feeling overwhelmed, start with the data mentioned earlier in this article. Once you have your baseline metrics, you can start to experiment with these tactics and see how they work for your brand. Everybody’s customer, product, and processes are different, so everyone will require a unique solution. And remember: If you want to discourage fraudulent behavior, you’ve got to get loud. ## In summary: how to prevent order and returns fraud A returns management system is essential for identifying suspicious return patterns and preventing fraudulent refunds. By tracking return frequency, item conditions, and customer behaviors, businesses can flag high-risk transactions before they lead to losses. #### 1. Integrate warehouse and order management systems for real-time visibility **‍**Returns fraud often slips through the cracks when inventory and order data aren’t fully synced. A warehouse management system (WMS) (ie., [ShipStation](https://parabola.io/product/integration/shipstation)) paired with an order management system ensures accurate stock tracking, reducing the risk of fraudulent returns and inventory discrepancies. #### 2. Leverage automation in returns management software **‍**Manually processing returns can lead to errors, lost inventory, and overlooked fraud. Returns management software automates key steps—such as return approvals, condition checks, and restocking—so businesses can maintain control and reduce fraudulent claims. #### 3. Optimize sales channel integrations (like Shopify & Amazon Seller Central) for better tracking and prevention **‍**A [Shopify](https://parabola.io/product/integration/shopify) or [Amazon Seller Central](https://parabola.io/product/integration/amazon-seller-central) integration with your WMS software or order management software ensures real-time return tracking. This prevents fraudsters from exploiting loopholes, such as claiming a return was never processed or attempting to return used items as new. #### 4. Improve 3PL coordination to reduce return abuse **‍**For brands working with third-party logistics providers, a [3PL WMS](https://parabola.io/blog/3pl-management) helps maintain data accuracy across fulfillment centers. Stronger coordination with 3PL partners ensures returns are verified properly, reducing return abuse and keeping operations running smoothly. By combining returns management software, order management systems, and WMS integration, eCommerce brands can reduce returns fraud, improve operational efficiency, and protect their bottom line. --- # The Future of Data Automation Marries Human Expertise and AI Technology Source: https://parabola.io/blog/the-future-of-data-automation-marries-human-expertise-and-ai-technology Determining how to deal with your ops data can make for a pretty messy equation. The tradeoffs feel inevitable: spending time on careful audits takes time away from strategic work, and the more bespoke your data processes, the less adaptable they are. [Alex Yaseen](https://parabola.io/), founder and CEO of Parabola, recently spoke with Sarah Barnes-Humphrey on the [Let’s Talk Supply Chain podcast](https://letstalksupplychain.com/episode-435-leave-excel-behind-with-parabola/) to dig into how data automation and AI are transforming the landscape for supply chain operators. The most striking revelation from the conversation? A staggering 67.4% of supply chain managers [still rely on Excel spreadsheets](https://www.supplychaindive.com/news/supply-chain-innovation-survey-BluJay-AdelanteSCM/530263/) to manage their operations. This level of manual work sounds unsustainable, but the stat tracks closely with Yaseen’s observations from his early-career consulting experience. “Even at Fortune 50 companies, in teams that were right in the spotlight, people were still doing incredibly non-scalable manual processes,” says Yaseen. That even high-achieving orgs are caught in anachronistic workflows begs the question: how did we get here, and how do we get out? ## Progress starts with people Although the answer to those questions might end with technology, it starts with the foundation of any organization: people. Call them “[operators](https://parabola.io/blog/parabola-is-for-operators)”: those “in-the-weeds subject matter experts who know where the skeletons are buried.” These people keep critical functions afloat, but sometimes they’re the only ones who know just how things work. Yaseen recalls working with a prominent healthcare company on a short-term project. A team processing claims relied on a single spreadsheet, nicknamed “Craigslist,” for routing inquiries. The sheet had a built-in algorithm that “magically” gave the correct outputs, but the only person who knew how it worked was the titular Craig, who had built it five years earlier, and had since left the company. This highlights a significant barrier to process improvement: knowledge retention. As Yaseen explains, “People end up being a huge part of this; somebody joins, they spend two years building all this knowledge, and if they don’t have a way to get that back out into the world of the company, when they leave [they take all that siloed knowledge](https://nobl.io/changemaker/how-to-retain-knowledge-when-an-employee-leaves/) with them.” Current tools like Excel are central to data automation, but tracking how workflows actually function is still a struggle. The Craigs of the world hold hard-won information in their heads — helping them document their [SOPs](https://parabola.io/resources/the-sop-community) should be the easy part. ## Tech that works for operators At the root of this is a mismatch in expertise. Time and again, the best salve for ops complexity is a tech remedy, but ops leaders rarely have the bandwidth to be tech mavens too.Yaseen highlights the occasionally fraught relationship between operations teams and IT departments: “Supply chain teams are always asking for more stuff, they’re trying new things…the tech team wants to say yes to everything, but they can’t keep up with the pace of how fast things are changing.“ The problem isn't that companies don’t want to modernize; rather, they’re often caught in a cycle of failed implementations and incomplete solutions. “They set up an ERP and they were told that an ERP is going to solve everything and it actually only solved maybe 10% of the most common use cases across the ops team.” Even the more bespoke tools are ill-equipped to deal with the complexity of crucial ops tasks. A purpose-built [PDF parser](https://parabola.io/blog/parsing-pdfs-with-parabola) works well when it’s looking at a document that has the same format every time, but that tool breaks when the invoice is “a screenshot of a photo that a truck driver took, of a crumpled piece of paper on their lap.” When inputs are so unpredictable, operators are forced to default to manual work. The ideal solution for these data automation challenges might be a more nimble, [no-code tool](https://www.nocodefinder.com/)that closely mirrors the processes that operators are already familiar with — something like a spreadsheet, but with the functionality for operators to show their work, and deal with the constantly shifting supply chain landscape. ## Onboarding user-friendly AI “As intuitive as Excel, but a little supercharged.” That’s how Yaseen characterizes [Parabola](https://parabola.io/product/overview/introduction-to-parabola). Although AI tooling is almost obligatory in new software, Yaseen highlights some of the ways that Parabola’s AI functionality aims to supplement human expertise, rather than supplant it. Built-in [auto-documentation](https://parabola.io/) is just one exciting feature Yaseen highlights. When you have data passing through a workflow on the Parabola canvas, the software automatically generates brief summaries, explaining what each step of the process is doing — whether extracting data from a messy PDF, or calculating discrepancies between rate sheets and actual invoices. So, if Craig is out sick, anyone on his team can understand his process, and keep his data automation running. Additionally, “we’ve prioritized AI features that help deal with unstructured data,” Yaseen explains, noting how Parabola’s AI uses vision technology and LLMs to [intelligently extract data from documents](https://parabola.io/), even when formats change or information appears in different locations. This is particularly valuable for tasks like [freight audits](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets) and invoice processing, where operators traditionally spend countless hours manually entering data. Parabola packages AI-assisted tools (that are actually useful) in a convenient no-code platform that looks like a flowchart. And unlike an ERP, it can quickly and easily integrate with your whole tech stack, [pulling data](https://parabola.io/docs/parabola-university/101/fundamentals/5-pulling-data) from sources in real-time, and sending it to the places where it matters most. ## What’s next As we move into 2025, Yaseen emphasizes that the greatest value is not in the technology itself, but in how it enables human expertise: “The hard thing is actually knowing the problem.” This is something AI still can’t do on its own. While AI and data automation are powerful levers, they're most effective when they enhance, rather than replace, people. The goal isn’t to eliminate spreadsheets entirely, but to automate data tasks so operators can focus on strategic decision-making and building personal connections. Yaseen envisions a future where working in operations feels like “having a really nice relationship that’s a little bit more human, a little bit more like casual conversation.” The calculations around data and supply chain operations are only getting more complicated, but the human element will always add value. --- # 7 Things Ops Leaders Are Doing That Their Finance Teams Love Source: https://parabola.io/blog/7-things-ops-leaders-are-doing-that-their-finance-teams-love "Supply chain people are starting to recognize that they deal in dollars," [Aaron Alpeter](https://parabola.io/the-supply-chain-tech-stack-report-lp), founder of supply chain consultancy Izba, recently told me. And he’s definitely not the only one feeling that way. From automated cost analysis systems to master COGS files, today’s ops leaders are speaking finance fluently and building bridges between execution and economics. They’re not just managing operations; they’re directly tying their work back to gross profit and creating more visibility for their companies along the way. The seven operations leaders featured in this article represent a vanguard of this new approach. Their strategies range from building KPI dashboards that [finance teams](https://parabola.io/solutions/finance) are “fighting to get access” to, to automated systems that eliminate month-end fire drills. They're backing requests with strong business cases and measurable ROI. And when finance comes knocking at their doors begging them to cut costs, they’re not just meeting their goals, they’re doubling them. Here’s what this group of finance-savvy ops leaders have to say about finding this kind of success at your org: ## On using tech to get visibility into true performance, cost, and flow of goods #### Matt Hertz, Founder @ ThirdPerson “As Mike Tyson astutely remarked, ‘Everybody has a plan until they get punched in the face.’ While this is true, it doesn’t encourage not planning! It is more important than ever to plan, anticipate, and build contingency in your operations. Changes are inevitable, particularly in this evolving time. The brands who maintain the most fortress operational (and financial!) positions are those who have not waited — not reacted — but who have been proactive in asking questions and developing Plans B, C, and D, so they can respond to any deck they are dealt. The most pressing dynamic that brands have been focused on is the potential change to tariffs and how that will impact global trade, production, and freight. Even more recently, a change in U.S. leadership will inevitably create new dynamics in global ecommerce. Depending on how these decisions play out, it will impact where product is manufactured, how it is transported, and where it is fulfilled — all greatly impacting costs. > Logistics is hard. Logistics is expensive. But visibility is power. Logistics is hard. Logistics is expensive. But visibility is power. Without total visibility on performance, cost, and flow of your goods, it is near impossible to manage your supply chain. Working with modern partners who are tech-forward, who can provide intelligence you need, and help make informed decisions, is key to successfully scaling your operations. On a more tactical level, the logistics ecosystem is quickly evolving. There are many new incumbents focused on certain segments or niches of supply chain, whether that’s fulfillment, freight, or small parcel and delivery. Embrace modern partners and leverage folks with specialties that overlap with your needs.” ## On building KPI dashboards that will make your finance team drool #### Bill Shube, Supply Chain Ops + Tech “We’ve recently kicked off an initiative to create a one-stop shop for all our supply chain KPIs. We’re focusing on defining them better, making them more easily accessible, and as automated as possible. When finance got wind of what we were doing, they were all over it. We’ve actually had to ‘fight them off,’ so to speak — there’s such a demand for this kind of transparency that they came to us with a laundry list of requests, we just couldn’t do it all at once. As we’ve gone through this exercise, we’ve realized that once you get the underlying data in place to support the top-level KPIs, there’s huge potential to drill into the data and build out lower-level operational metrics to support the rest of the business. It’s a lot of work to put it all into place, but will make reporting so much easier once it’s done.” ## On doubling savings initiatives in 2025 #### Keith Frymark, SVP Supply Chain + Quality @ Seed Health “We built a master COGS file (it's our operational COGS library/bible), but finance has this file open at all times to help inform their financial modeling and P&L. It's been a great tool for both teams. The file is built by SKU by channel and breaks out by month and by year the specific costs for raw materials, packaging, labor, kitting, freight, etc. If we are modeling costs for a new product or say marketing or sales need to check the cost of a SKU, they can easily find the information without having to bother ops. Finance has tasked our team with some pretty hefty savings initiatives in 2025 — not only are we going to be able to hit the goal, we have the opportunity to double it! Finance sure loves that. These processes and initiatives have established a high level of trust from finance. Operations and finance go hand in hand, I find great operational leaders also have a high financial acumen making it easy to partner with finance.” ## On slicing and dicing the data and making budgeting a breeze #### Paige Zachs, VP of Supply Chain + Ops @ Coterie “Because we have automated almost all of our variable cost analysis using [invoice data](https://parabola.io/use-cases/invoice-parsing-line-item-categorization) in Parabola, 2025 planning and budgeting has been a breeze! > We now have so much cost visibility, allowing us to slice and dice the data in an endless number of ways and highlight margin opportunities. We now have so much cost visibility, allowing us to slice and dice the data in an endless number of ways and highlight margin opportunities. Even though we have an ERP and BI tool, there are still so many things that get done in Excel and Google Sheets and we’ve been showcasing the power of Parabola to our finance and wholesale teams who are looking to leverage this tool as they build their budgets and P&Ls too.” ## On the need for increasing cross-functional forecasting #### Aaron Alpeter, Founder @ Izba “I think the biggest thing is that supply chain people are starting to recognize that they deal in dollars and need to have a good interface with finance folks — and finance folks are recognizing that units and things that happen in the supply chain world also matter in a spreadsheet. So what I’m seeing is that there are tighter linkages around annual planning, around month-end where people are starting to have some conversations to say, ‘Hey, you keep asking me for the same report every month. What is it that you need and when do you need it?’ They’re just making those workflows part of the process versus having more of a fire drill every month. I’m also seeing that people are being much more explicit in how they’re building their forecast. Teams are starting to look at an integrated S&OP plan and say: ‘What is this difference between what I want to have happen and what I think is going to happen? And then how do I make sure that we’re being as aggressive as we should be in our forecast?’” ## On funneling actualized logistics costs into your ERP with supply chain automation #### James Hargett, Director of Ops “One thing we recently did was leverage Parabola to get our actualized logistics costs into our ERP, which not only helped our team track but also gave finance that data point so they could use that instead of having to pull invoices to validate at the end of the month. Another thing we were doing was regularly adjusting any inventory variances so that we didn’t have any large adjustments at the end of the year or surprises when it came to doing our full physical inventory counts.” ## On building strong business cases when requesting resources #### Jennifer Renner, Senior Manager of Vendor and Sample Operations @ Stitch Fix “My teams and I are dedicated to driving efficiency and maximizing our current resources to manage growth without automatically increasing headcount. We rigorously evaluate end-to-end processes, identifying gaps and modernizing workflows where possible. Any request for additional resources is backed by a strong business case and measurable ROI, showing that we’ve pursued all internal solutions first. My finance partners trust that if I’m asking for something, it’s because I’ve already thought through every opportunity, clearly outlined the options, and demonstrated why adding spend is critical and other approaches won’t achieve the same results. This reinforces my commitment to strategic, cost-effective improvements that truly support our bottom line.” --- # Pair Eyewear Saves $50k Annually by Automating Their Inbound Po Process Source: https://parabola.io/blog/pair-eyewear-saves-50k-annually-by-automating-their-inbound-po-process In the [growing](https://www.grandviewresearch.com/industry-analysis/eyewear-industry#:~:text=The%20U.S%20eyewear%20market%20is,contributing%20to%20the%20market%20growth.) world of direct-to-consumer eyewear, Pair Eyewear has made a name for itself by giving consumers what they’re looking for: optionality. Not only are their frames customizable, they’re also priced so that you never have to get stuck in an eyewear rut: Instead of relying on one pair of glasses, Pair Eyewear makes it possible to have a pair for a special night out, your favorite season, and for everyday use. They’ve even collaborated with [major museums](https://paireyewear.com/top-frames/the-met) and [beloved franchises](https://paireyewear.com/top-frames/harry-potter) to bring frames with tons of personality to market. But with the success they’ve found since launching in 2019, they’ve felt some natural growing pains — this is something that became particularly apparent when they expanded their wholesale business and were receiving tons of inbound PO PDFs via email. Between hard-to-access data and PDFs clogging their inboxes, Zach Wilner, who leads data and analytics at Pair, and Zach Headapohl, the Senior Manager of CX Operations, were in the market for a purchase order management solution. ## The challenge: inboxes full of unstructured data Wilner on the data side remembers the early days of their growth all too well. “We were using Narvar as our [returns management](https://parabola.io/use-cases/returns-management) system,” he recalls, and they needed to be able to pull data from that system for internal reporting. But there was a major limitation: They could only export data as a CSV or send to an email address. This seemingly small issue created a significant bottleneck in their data pipeline, preventing the team from accessing crucial information about orders in real time. Meanwhile Headapohl on the ops side was grappling with a different set of problems. As his team expanded, so did their need for analytical firepower. “We needed a sandbox,” Headapohl explains, “somewhere we could prototype and iterate without constantly competing for engineering resources.” The operations team found themselves in a constant tug-of-war, trying to balance their growing needs with the limited availability of technical team members. And as Pair expanded their wholesale business, they faced a new hurdle: an influx of purchase orders arriving as messy, hard-to-work-with PDFs. The volume was so high that they were considering hiring a full-time employee just to manage the process of getting that data cleaned and sent to their 3PLs. They needed a new approach to purchase order management. ## The solution: automated purchase order management When Wilner first discovered Parabola, it was through Narvar, their order management system. They were unable to get the data from that platform into Snowflake, so they were looking for something that could push [data from email](https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-email-data) to S3 (they had a home-grown ingestion system from S3 into Snowflake). But what started as a solution to one specific problem soon blossomed into something that has become a sort of foundational tool for the ops team. Headapohl and his team quickly realized Parabola’s potential beyond simple [integrations](https://parabola.io/product/integrations). It became their sandbox — a place where they could rapidly prototype and build automations to support their processes and data needs without relying on engineering resources. It essentially created an army of citizen developers out of mostly non-technical ops folks. For Headapohl, the full scale of Parabola’s power became apparent as he worked through a hefty project in which he was trying to merge returns data with quality data: “So all of this would be pretty easy to do in Excel if I had a super computer…but I’m dealing with like a half a million records across 36 different fields in some cases, and am standardizing them so I can stack them and then create unique identifiers.” “To have something to power that is pretty rare,” Headapohl explains. Most recently, they’ve been using Parabola’s [PDF parsing](https://parabola.io/blog/best-methods-pdf-parsing) capabilities to solve Pair’s wholesale purchase order management challenge. Typically, Pair receives PDF POs from retailers via email. They had to manually convert them to .csv so they could 1) send it to the 3PL and place the order for shipment and 2) let their planning team know how much inventory is going out the door so they can track revenue metrics. Because their wholesaler doesn’t have EDI or any electronic transfer capabilities, they were stuck in a very manual process. This led them to scope Parabola’s [PDF parsing](https://parabola.io/blog/parsing-pdfs-with-parabola) step (which combines OCR vision technology with AI to read and contextualize PDFs with extreme accuracy) to see if it could start doing this work for them. Spoiler alert: It could. Here’s how they’ve now automated this whole process — instead of hiring someone to manage it full time: “It’s been very, very accurate from day one. I actually barely put any prompts into the AI — I just kind of turned it on, added the columns I wanted, and set it free. It’s a really big unlock for us,” Wilner said. “These PDFs can be six pages of really poorly formatted order data that can be hard for even a human to read.” ## The results: 5 key workflows automated As Pair Eyewear continues to grow and evolve, Parabola has become an integral part of their technology stack. “We can’t ever rip this out, the ops team has become so dependent on it,” Wilner says. This only seems to improve his working relationship with Headapohl and ops as the person leading data at Pair. Pair’s experience with Parabola started with a simple Flow to solve a critical (but simple) problem they were facing. Now? The whole ops team is enabled with a data tool that gives them the power to automate their workflows, clean and access their data, and iterate on team-wide processes without the support of engineering or data teams. Plus, Pair can report: And the benefits don’t always have to come from the most complex use cases according to Headapohl : “some of the stuff we do with Parabola is really simple, but tedious to execute manually in Excel. I think we’ve all asked, what is the one thing that is keeping this data from pivoting or merging the way that I need?” To bring us home, Wilner told me about someone unfamiliar with Parabola that they brought into the PDF project and immediately threw into the weeds. His takeaway? “This tool rocks.” --- # Peak Season Prep: How 10 Ops Leaders Across Ecomm and Supply Chain Get It Done Source: https://parabola.io/blog/peak-season-prep-from-10-ops-leaders When asked what unique challenges this peak season has been bringing for brands, supply chain expert [Matt Hertz](https://parabola.io/) had a laundry list of potential disruptions vexing his clients: inevitable changes to the de minimis laws, shipping parcel carriers abruptly going out of business, port strikes, and uncertainty around November’s election were just a few. And headlines would suggest that these are *all*viable concerns for any brand that needs to get goods to a customer — or any business that’s helping *move*that freight. We checked in with Parabola’s advisors, a group of ops leaders across industries, for their thoughts: What did you do well this year in preparation? What challenges does the current landscape present? Anything you wish you had done differently in hindsight? Overall, there’s a collective wish for a crystal ball. Folks are wondering how they could have predicted the east coast strikes. Is it possible to get ahead of parcel rate increases? Which way will relevant legislation turn? “It’s hard not being a pessimist working in global supply chain,” Paige Zachs, VP of Supply Chain + Ops at Coterie, told us in response to the hard-to-predict challenges that seem endless. But the good news is that there’s also been a lot of positive preparation keeping everyone on top of the challenges at hand. Automation hacks, a reliance on data, and being proactive whenever possible are just a few things that have helped this group stay resilient during this time. Here’s what else they had to say: ## On how brands are tempering expectations and proactively safeguarding profitability #### Aaron Alpeter, Founder @ Izba “One of the most interesting trends I’ve seen is that many brands are tempering their expectations for this peak season, focusing on profitable growth rather than growth for its own sake. Several nine-figure brand CEO’s I’ve spoken with are incorporating risk into their plans due to the stupid amount of money flowing into digital channels around the election. After the election, all of the pent-up brand marketing budgets are likely to be funneled into Black Friday and Cyber Monday deals which will keep CAC’s elevated this year. Smart brands are taking proactive steps to safeguard their profitability this year. I’m closely watching how under-a-pound parcel networks perform this year. The USPS decision to effectively eliminate the workshare program has already led consolidators like Pitney Bowes to discontinue those operations, causing a significant volume of shipments to seek new providers just before peak season. These networks may not be prepared for the added strain. If I could go back, I’d advise brands to establish more distribution points to be closer to their customers and reduce reliance on these networks.” ## On keeping customer feedback top of mind — and aligning with your 3PL on corrective actions #### Alex Kazickas, VP of Ops @ Primary “I’m a big believer in ’What gets measured, gets improved.’ So in prep for peak season, we better consolidated customer feedback data to track and discover shipping and fulfillment error trends. Partnering with our 3PL, we held weekly meetings reviewing this data, identifying the root causes, and aligning on corrective actions. These improvements across multiple issues will have a significant impact both internally and with our customers, especially as order volumes scale in Q4. The landscape of supply chain and logistics is constantly changing. From unpredictable peak parcel surcharges to port strikes, it’s increasingly important not only to be aware of potential risks as early as possible, but translate them to concrete metrics that clearly show business impact. This year has been a good reminder of the importance of investing in industry insights and creating impact models so businesses can make the best possible decisions.” ## On on-site 3PL kickoffs and prepping for what happens post-peak #### Lindsay Keys, Director of Logistics @ Brooklinen “We host on-site kickoffs with our 3PLs every year to align on all operational aspects that occur within the facilities (order + receiving forecasts, carrier pickups, labor + shifts, packaging, cycle counts, etc). We love to get the energy + excitement building with the teams that get our inventory in and out the door. We also have constant conversations about what happens after peak. It’s important to make sure we don’t take our foot off the gas after peak, but also feel prepared about our strategy (versus building it during our pop). Overall, peak is always on our mind — we focus on continuous optimization throughout the year to ensure we can execute strong and deliver for our customers!” ## On collaborating with internal stakeholders to align on Q4 expectations #### Keith Frymark, SVP Supply Chain + Quality @ Seed Health “We’ve been planning ahead for potential east coast port strike, escalating risks in monthly S&OP and building up inventory ahead of time, as well as sharing growth and order plans with warehouses to prep labor needs. We also host cross-functional meetings with growth, marketing, brands, leadership to align on expectations for Q4/peak in advance for ops to support and not be blindsided by unexpected events.” ## On juggling the many macro variables at play #### Matt Hertz, Founder @ ThirdPerson “This peak season is a little peculiar (but don’t we say that every year?!) in that there’s a number of massive macro variables at play. Brands I am working with are dealing with anything from inevitable changes to the de minimis laws, to shipping parcel carriers abruptly going out of business, potential port strikes, and uncertainty around November’s election and what it means for trade/business in 2025 and beyond. Any of these headlines has the potential to severely impact how brands operate, which adds uncertainty, risk, and cost to an already sensitive supply chain The ’smartest’ brands are aware of these issues and are working to minimize the adverse impact through building contingency/redundancy into their supply chains. Problems become solvable when you are able to plan and bake in reserves for unforeseen circumstances. I wish I knew with certainty what would happen with global trade/sourcing/manufacturing in the next 12 months. The uncertainty around what might happen is already creating a ripple effect with high costs of air/ocean freight, backlogs at the port, and a renewed interest towards onshoring. The ability to anticipate and accurately predict changes and outcomes is beginning to separate those brands who turn profitability, with those who are challenged by excessive costs.” ## On using forecasts to negotiate with supply chain partners #### Paige Zachs, VP of Supply Chain + Ops @ Coterie “Diapers aren’t a seasonal product, nor do we really run promotions as a business. Also, because the majority of our customers are subscribers who stay with us for a long time (through potty training!),our business is highly forecastable. Therefore, we use our accurate forecasts to our advantage when negotiating with supply chain partners. For example, we were able to negotiate discounts off the typical peak season surcharge issued by parcel carriers precisely because our business is not seasonal and we provide accurate forecasts. Overall, I wish I could crystal ball global supply chain better! I did not anticipate how steep the UPS holiday surcharges would be this year nor did I anticipate another port strike. It’s hard not being a pessimist working in global supply chain!” ## On getting proactive with technology #### James Hargett, Director of Ops @ Chubbies “One thing that we’ve done is proactively build out some flows in Parabola to track inbound associated with the peak period so we have our finger right on the pulse of the status of those goods. And one thing I wish we had done differently is position some of our markdown inventory more strategically in our two DCs to help minimize the impact of split shipping a bit more.” ## On stress testing revenue forecasting and supply planning processes #### Patrick Ringston, COO @ Optech Group, former Casper + Blue Apron “Planning, planning, planning…I spent the better part of the summer working with a client to establish and stress test their revenue forecasting and supply planning processes. We developed a baseline model along with optimistic and conservative views and landed on what level of tolerance we had for a miss (i.e. high and low) to ensure we had the right supply and safety stock heading into the most crucial time of year. There’s nothing I wish I had done differently, per se, but what I see very often with clients is they tend to think of peak as this isolated event that we need to start working on in the summer. A lesson I learned early in my career when I was at UPS was that peak planning starts on January 1st and it’s the crescendo that we are constantly building and prepping for.” ## On understanding what internal stakeholders really need #### Bill Shube, Supply Chain Ops + Tech “We have better visibility into the health of our inventory than we ever have before. Lots data flowing, all right at our fingertips. I wish we had done a bit more to pin down our stakeholders to understand exactly what they need to do their job better. We have a general sense and will be able to respond to last-minute requests because we have so much data flowing now — but the ‘analytics final mile’ is always complicated. The more we know early about their exact needs, the better we can deliver to their needs.” ## On automating as much as possible to create “heartbeat reporting” #### Nate Peterson, VP of Ops and Logistics @ Tecovas “We are working to automate all our daily shipping, throughput, and overall state of our distribution center. We are calling this our heartbeat reporting. This should be something we have in place in the next few days. Port strikes are not ideal. We are working to do a better job with visibility on our entire supply chain and leaning in on our supply to demand planning with our launch in the wholesale channel.” ## The TL;DR: 10 essential lessons from supply chain leaders ‍Peak season can make or break your operations. Drawing on advice from 10 seasoned operations leaders, here are 10 actionable tips to ensure your business is ready to handle the rush: #### 1. Plan well in advance **‍**Start your preparations months ahead. Develop detailed forecasts and create a roadmap that anticipates demand spikes, ensuring you allocate resources effectively before the rush begins. #### 2. Leverage automation and technology **‍**Invest in robust systems such as [warehouse management software](https://parabola.io/) and order management solutions. Automating processes can reduce errors, improve efficiency, and free up your team for strategic tasks. #### 3. Optimize inventory management Keep a close eye on inventory levels. Utilize [data-driven insights](https://parabola.io/use-cases/inventory-reconciliation) to prevent stockouts and overstocking, ensuring you meet customer demand without tying up excess capital. #### 4. Strengthen supply chain partnerships Build strong relationships with suppliers and [3PL providers](https://parabola.io/blog/3pl-management). Collaborative partnerships ensure timely restocks and help you handle unexpected fluctuations in demand. #### 5. Enhance returns management processes With peak season often comes a surge in returns, so streamline your returns process. Establish clear policies and deploy efficient [returns management systems](https://parabola.io/use-cases/returns-management) to handle volume and reduce fraud. #### 6. Boost customer service readiness Anticipate a spike in customer inquiries. Enhance your support channels, provide clear communication, and train your team to resolve issues quickly, ensuring a seamless customer experience. #### 7. Prepare your workforce Upskill your staff and consider temporary hires or extra shifts. A well-prepared and flexible team is key to managing increased workloads during peak periods. #### 8. Monitor key metrics in real-time **‍**Use dashboards and data analytics to keep an eye on performance indicators. Real-time monitoring enables quick adjustments to strategies, keeping operations running smoothly. #### 9. Integrate cross-channel operations Ensure your ecommerce, ERP, and warehouse management systems work together seamlessly. A unified, cross-channel approach minimizes friction and supports efficient order processing. #### 10. Review past seasons and adapt **‍**Conduct post-season analyses to learn what worked and what didn’t. Use these insights to refine processes and bolster your strategy for future peak periods. --- # Ask Keith Frymark, SVP of supply chain at Seed Health, anything Source: https://parabola.io/blog/ask-keith-frymark-svp-of-supply-chain-at-seed-health-anything --- # Automate and analyze your Shopify data with Parabola Source: https://parabola.io/blog/how-to-actually-leverage-your-shopify-data --- # How ops, logistics, and supply chain teams use Parabola Source: https://parabola.io/blog/how-ops-logistics-and-supply-chain-teams-use-parabola Every Tuesday at 12PM ET, join Parabola’s resident product expert Adam Reisfield as he gives an overview of how companies like Flexport, Brooklinen, and Rhone use Parabola to build automated processes centered around the data they rely on every day. We’ll cover all of the Parabola basics you need to know, plus how to: - Clean, combine, transform and reconcile data from multiple sources in our no-code data canvas (across ERPs, WMSs, PDFs, spreadsheets and others) - Leverage AI to extract, categorize, and standardize data from PDFs and emails - Automate and codify SOPs for data audits and reconciliation at scale - Save costs and meaningfully tie your work to revenue through a variety of use cases like carrier scorecarding, order and PO management, inventory reconciliation, sourcing and production reporting, and more Register below to save your spot. ‍ If you’re looking for more information on the product, use cases, or best practices, don’t hesitate to [reach out to our team for a customized demo](https://parabola.io/blog/how-ops-logistics-and-supply-chain-teams-use-parabola#wf-form-get-a-demo-form). --- # How to automate freight audits with Parabola Source: https://parabola.io/blog/how-to-automate-freight-audits-with-parabola --- # Ask Matt Hertz, ecomm ops expert, anything Source: https://parabola.io/blog/community-ama-ask-matt-hertz-ecomm-ops-expert-anything ‍ Matt Hertz is the co-founder of Third Person, a platform where brands can match with 3PLs that actually fit their needs, and the founder of Second Marathon, which similarly helps brands find outsourced fulfillment providers that are suited to support their businesses. Before building Third Person and Second Marathon, Hertz spent time working in house at brands like Rent the Runway, Birchbox, and Shyp, where he helped oversee (and grow) warehouse operations and global supply chains for logistically complex ecommerce companies. He's passionate about implementing processes and systems that deliver amazing customer experiences and has experience scaling startups and managing ops at billion dollar businesses. ‍ --- # How companies of all shapes and sizes reconcile and compare inventory levels with Parabola Source: https://parabola.io/blog/product-demo-reconcile-and-compare-inventory-levels-with-parabola Learn how leading brands automate their inventory consolidation and reconciliation processes. In this video, you’ll see how to: - Leverage automation and AI to combine data from PDFs and spreadsheets with ERPs, WMSs and other systems - Reconcile, report on, and gain visibility into inventory data from multiple sources and DCs - Compare your inventory on hand with data from other sources, like sales in Shopify or spreadsheets from vendors If you’re looking for the inside scoop on any of our other features, use-cases, or best practices, don’t hesitate to reach out to our team with suggestions. --- # How operations, supply chain, and logistics teams save time and money with Parabola Source: https://parabola.io/blog/product-demo-parabola-overview See how companies use Parabola to combine the data running through their company and create automated processes. We’ll be showing how leading brands like Brooklinen, Rhone and Seed Health: - Combine supply chain data from multiple sources (across ERPs, WMSs, PDFs, spreadsheets, email and others) - Automate SOPs for data audits and reconciliation at scale - Save costs and accelerate revenue through improved freight audits, on-hand inventory reporting, PO & order management, and more - Leverage AI to extract, categorize, and standardize data from PDFs and siloed sources If you’re looking for the inside scoop on any of our other features, use-cases, or best practices, don’t hesitate to reach out to our team with suggestions. ‍ --- # How to use AI to parse and extract PDF data at scale Source: https://parabola.io/blog/product-demo-how-to-use-ai-to-parse-and-extract-pdf-data-at-scale On Thursday, May 30th at 1PM ET, we explained how companies like Flexport, Brooklinen, and Uber Freight use Parabola’s AI to extract data from PDFs, transform it into the formats they use internally, then embed it into their existing processes. The newest version of this feature gives users the ability to put data from even their messiest docs into action using AI-powered PDF parsing. In the past couple of months alone, our customers have parsed almost 100k+ pages combined. What’s so cool about our PDF feature? And why is it different from others on the market? - Our unique application of AI combines OCR with a vision-based LLM. This means that we combine razor-sharp imaging capabilities with AI’s ability to contextualize information. - We provide one-step import with virtually no configuration needed, even when there’s variability across PDF formatting. - Your data is ingested into a spreadsheet format — and can be transformed then sent back into the systems and tools you use. If you’re looking for the inside scoop on any of our other features, don’t hesitate to reach out to our team with suggestions. [Parse your messiest PDFs using AI](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet)*– and*[extract data](https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-pdf-data)*– using Parabola.* --- # On Parabola’s Mission to “beat the Drum” for Operators Everywhere Source: https://parabola.io/blog/parabolas-mission-to-beat-the-drum-for-operators-everywhere Today Parabola finally gets to show off our new brand (a refreshed website, logo, messaging, and more). I can’t wait to tell you about it, but first, I want to share a little context around why this feels like such a big deal. Years ago, I was working as a strategy consultant when I was hit with a realization: All of the problems my firm was hired to solve had solutions that were perfectly obvious to the people closest to them. The knowledge point people — those teammates who understand how processes were built along the way and can always answer that one obscure question — could tell me exactly what was wrong and how to fix it. But when it came to actually implementing that change, their hands were tied, left without the budget, bandwidth, or technical expertise needed to make it happen. That’s why I started Parabola. I wanted to give those knowledge point people a product where they could build the logic behind their most complex workflows and solve the problems that took up so much of their time. But amidst these grand ambitions, we’ve struggled to make it clear what we are and who we’re for, let along the awesome things we’re up to behind the scenes. Today, we’re changing that. ## “Say the thing” After a bunch of hard work over the past few months, we finally have a brand identity I’m proud of — and more importantly, an identity that represents the huge impact customers like Uber Freight, Brooklinen, Flexport, and many more are having as they equip their teams with Parabola. Historically our external facing presence has been more of a Rorschach test: Whatever pre-existing context you’re coming in with, you can project that onto Parabola. Ultimately, this is the challenge of building such a widely applicable tool: We could market it as a Swiss Army Knife, capable of taking on any task at hand. But ultimately we decided that a successful outward-facing version of Parabola is actually more polarizing than it’s been, making the people who it *really*resonates with go, “where has this been all my life?” In the proverbial writers’ room, we adopted a motto: “Just say the thing.” Any time we were trying to describe what we do or relying too heavily on tired SaaS jargon, one of us would prompt the group to*just say the thing.* It sounds almost hilariously simple, but it was one of the most important exercises in getting clarity for our customers and prospects. The hardest line to nail was Parabola in a sentence, but I’m excited to share where we landed with you all: > Parabola is the spreadsheet alternative where you combine the data running throughout your company and create automated processes. It’s exciting to finally bring our new brand to market, and we’re celebrating that in a few different ways. ## If you think it, you can build it Our users come up with the most innovative solutions I’ve ever seen. We recently had a cookware brand use Parabola to analyze shipping discrepancies across carriers and automate email outreach to those carriers with summarized details —something they were doing by hand 3-times per week until then. I also just heard about a CX leader using Parabola to analyze Zendesk tickets against 3PL data to produce visibility into shipping issues at his company. They really bring to life our new motto: If you think it, you can build it. In tandem with this new brand for Parabola — and in honor of all of the badass operators who’ve brought truly creative solutions to life — we’re hosting a webinar highlighting 5-minute case studies from seven ops leaders across ecommerce, freight, and logistics. They’re going to tell quick-hitting stories behind some of the professional challenges they’ve faced (and the solutions they ultimately deployed) at companies like Flexport and FabFitFun. [You can register here](https://parabola.io/). ## Creating a community for knowledge point people At Parabola, every decision we make is meant to champion and help our customers. We help equip and enable them, but ultimately we’re just a tool they use (and love). They’re the hero. And even more strongly than we realized, the knowledge point people we work with are not just underserved by tools, they’re also lacking in community. We recently started hosting city-specific, small-format dinners for customers and prospects, and the result has been clear: Supply chain and logistics leaders are eager to connect and talk shop with one another. I’ve attended a few, and once chatted with folks looking for best practices sourcing luxury materials without a single point of failure. Others are wondering how seriously to take customs issues resulting from international trade disputes. And some are deep in the throes of tool implementation and need to hear success stories from the other side. We’re excited to announce the creation of [The SOP Community: Standard Operating People](https://parabola.io/resources/the-sop-community) as a space for these people to grow in their careers and networks. It’s a place to ask hard questions, get smart answers, and source tactical advice for improving both day-to-day inconveniences and long-term strategy. The SOP Community****will take place online through Slack and webinars and also through in-person dinners, happy hours, and fireside chats. It’s a totally sales-free zone: The goal is to help people working in this space get as much value from one another as possible. More info and applications to join are open on our site, and I’d encourage leaders working in supply chain, logistics, and operations to get involved. [Apply here](https://parabola.io/resources/the-sop-community). ## Looking forward In the past, we struggled with how to walk the line of talking about the right level of specificity while still talking about the grandest future vision, but the reality is that being specific and having a point of view on how we differentiate in the world is actually how we help *become* a part of the biggest possible future for knowledge point people. To successfully create that future, we’re going to be louder and in more places driving awareness. We’re here to beat the drum publicly on behalf of operators everywhere. – [Explore our new website](https://parabola.io). [Register for If You Think It, You Can Build It](https://parabola.io/). [Request to join The SOP Community](https://parabola.io/resources/the-sop-community). --- # If You Think It, You Can Build It Source: https://parabola.io/blog/webinar-if-you-think-it-you-can-build-it On May 23rd at 12PM ET/9AM PT, we hosted “If you think it, you can build it,” a virtual panel where ops leaders showed behind the scenes takes on how they’ve solved some of the toughest challenges in their careers at companies like Flexport and FabFitFun. From how to get so ahead of track and trace that you’re alerting your 3PL of issues they don’t even know about to leveraging AI to extract important supply chain information, the topics are super relevant for anyone who has the words “supply chain” in their job description, and the recording is now live. ## If you think it, you can build it Featuring: - Gilles Lagast (Global Director, Business Operations @ Flexport) - Bill Shube (Supply Chain Ops & Technology @ Lego) - Agla Fridjons (Senior Director of Ops) - Andreas Andrea (former Director of Logistics @ FabFitFun, Murad) - Matt Hertz (Co-Founder @ Third Person, former Rent The Runway) - Matthew Chapa (Transportation Manager @ Crunchyroll) - Dyci Sfregola (Founder @ New Gen Architects, former Anaplan, Unishippers) --- # JustFoodForDogs fills an additional $7 million in orders annually by automating inventory management Source: https://parabola.io/blog/justfoodfordogs-fills-an-additional-7-million-in-orders-annually-with-automated-inventory-reporting [JustFoodForDogs](https://www.justfoodfordogs.com/) is a nutrition-first dog food retailer that manufactures nourishing products designed to promote overall pet health and longevity. They are hyper focused on the quality of their food, providing customers with “the best food in the world for your dog.” Leading the fulfillment team at JustFoodForDogs, Harry Hartman was responsible for overseeing the company’s direct-to-consumer fulfillment team, which means managing their 3PL network, end-to-end inventory management, and executing seamless product delivery to customers. He has since transitioned to managing the team’s B2B operation, delivering to retail partners. As demand for JustFoodForDogs grew across multiple retail channels, scaling the operation came with a host of data challenges. Hartman was in search of a flexible data tool that could automate and improve a range of operational processes. Parabola fit the bill. ## The challenge: manual, error-prone inventory management While the team at JustFoodForDogs was excited that order volume was steadily growing, inventory management became increasingly difficult as the business scaled. The fulfillment team was manually completing a 58 page SOP that would take 8 hours to complete just to get an up-to-date count of their inventory levels. This highly manual, laborious process left plenty of room for human error: It required cleaning and matching data from 20+ inventory locations including production facilities, fulfillment centers, and bulk storage locations. With such a manual [inventory reporting and management](https://parabola.io/blog/automated-inventory-reporting) process, errors were inevitable; which at best meant unnecessarily ordering and producing materials, and at worst, resulted in stockouts that directly impacted customers. “We were making the manual way of tracking inventory work for as long as we could, but once we started to see an uptick in calls at our customer support center, the alarm bells went off,” Hartman explained. To make matters worse, the reporting process was so manual and convoluted that only one Inventory Specialist, Joseph Oca, knew how to run it. Hartman shared that, “There was only one person who knew how to get the data output we needed, which meant he couldn’t take PTO without impacting the entire company. The process was not scalable for our team.” The fulfillment team also needed to get their spend under control. “We were doing everything we could to deliver a positive customer experience which often meant expediting shipments or over-purchasing products. It got really expensive,” Hartman said. With a vast majority of their products requiring refrigeration to stay fresh, the team was over-purchasing dry ice to make sure they never ran out. > “The worst part about all of our fulfillment issues was that it was limiting our ability to grow the business. We were constantly playing catch up and couldn’t focus on growth,” Hartman explained. Without accurate forecasts, they were stuck paying for expensive storage and wasted products. While their workarounds often solved the customer impact issue, they created unwieldy expenses that made it hard to plan a fulfillment budget and pursue additional sales channels. ## The solution: automated fulfillment processes After a careful review process, the team at JustFoodForDogs chose Parabola to introduce automation to their fulfillment processes. They have built workflows that seamlessly blend rows of data from multiple sources: [WMS data](https://parabola.io/) from their 3PLs, purchase order details from Netsuite, a demand forecast from the planning team, QC inventory data from the quality control team…the list goes on. By integrating data from their co-manufacturers and [3PL partners](https://parabola.io/blog/3pl-management) with pertinent information from different internal teams, they successfully automated [inventory consolidation](https://parabola.io/events/how-companies-of-all-shapes-and-sizes-reconcile-and-compare-inventory-levels-with-parabola) reporting. Oca, the employee previously responsible for the tedious reporting process, was a fast learner—and he was excited to automate the entire process using Parabola. He created an Inventory Advisory workflow that outlined average weeks of supply, total weeks of supply, and compared that data to order status. And to address forecasting needs, they created an Ecommerce Dashboard, which allowed them to monitor volumes of dry ice and perishable food products in stock, and determine when to purchase more and how much to purchase. The team was initially concerned that it would take them months to learn how to use Parabola, but they quickly became expert builders.Their only regret? That they didn’t implement Parabola sooner. ## The results: $7 million in additional revenue, yearly Since implementing Parabola, the fulfillment team at JustFoodForDogs is finally able to contribute meaningfully to revenue growth. They calculate that Parabola has helped them fill close to $7 million in additional revenue they ship out each year. With Parabola, the team has automated fill rate reporting and increased fill rate at their largest retail partners by 15% to an impressive 98%. That 15% increase in fill rate correlates to an additional $130K in revenue they can ship out each week without having to hire additional team members. Additionally, the increase in fill rate has allowed the team to confidently pursue new sales channels without growing headcount. Hartman shared: “Automating inventory management in Parabola has been a huge unlock for growing our business, we can finally keep up with demand and plan for future growth.” The fulfillment team is no longer scrambling to get an accurate inventory count or build reliable forecasts for inventory needs, packaging needs and overall budget requirements. They’ve dramatically cut down on costly last-minute product and packaging shipments to manufacturers and 3PLs, and they’re spending less on dry ice each month. Hartman shares: “Our team is proud to be delivering on the customer experience while keeping costs under control.” The fulfillment team at JustFoodForDogs is feeling much better about scope since Parabola has been implemented, saving 64 hours each month on the inventory consolidation report alone. They now have over 120 workflows running in Parabola for different use cases, and the majority of their operations team is in Parabola each week. And as for Joseph Oca who felt like he couldn’t take PTO because he had to own reporting? He finally took that vacation. --- # Action on Your Messiest Data With AI-Powered PDF Parsing Source: https://parabola.io/blog/ai-powered-pdf-parsing When it comes to technology in the workplace, we’ve come a long way. Colleagues can connect in an instant from across the globe, there’s a software product for just about every process imaginable, and collaboration tools make it easy to reach decisions quickly. Despite all the progress that’s been made to transform the way we work, outdated PDFs remain ubiquitous in business. Whether you’re auditing invoices, [digitizing packing lists](https://parabola.io/tool/use-ai-to-convert-data-from-packing-lists-to-spreadsheets), or reconciling delivery receipts, chances are, you’ve come across your fair share of PDFs. There are 2.5 *trillion* PDFs circulating across platforms globally, and more often than not, humans are reading, organizing, and transcribing critical information from those documents. It pains us to think about all the errors made and the hours wasted. We’ve been determined to put an end to the whole “copy and pasting data from PDFs” thing for a while—last year, we introduced [PDF Parsing with AI](https://parabola.io/blog/parsing-pdfs-with-parabola) so you would never have to manually review and input data from another PDF again. But now, **we’ve completely upped the ante. ** ## Why Parabola’s PDF parser is different Unlike most [PDF parsers](https://parabola.io/blog/parsing-pdfs-with-parabola) on the market,**by combining multiple cutting-edge parsing approaches,**[Parabola’s PDF parser](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet)**can now handle even your gnarliest, clunkiest PDFs with virtually no configuration. Even when there's a lot of variability from individual PDF to individual PDF.** We don’t just recognize your document, we convert it into *usable* data in a spreadsheet-like table. Most [PDF parsers](https://parabola.io/blog/best-methods-pdf-parsing) are brittle, struggling to process any unpredictable inconsistencies like page breaks, oddly formatted tables, and metadata mixed with tabular data. They may require upfront configuration that needs some level of technical expertise, a large set of example documents, and users to spend time training the software. And even with all that setup, your typical [PDF parser](https://parabola.io/blog/parsing-pdfs-with-parabola) can’t contextualize PDF data as well as a human can. We’re changing all of that. ‍**Now marrying two elements of AI—OCR and a computer vision enabled LLM—Parabola’s PDF parser facilitates a smart, one-step import to quickly and accurately parse any PDF without hours of training.**Combining battle-tested OCR technology with cutting edge multi-modal LLMs, means you never have to think about manually uploading or configuring PDF data again. And you don’t need to worry about where the information lives in your document because Parabola will detect it for you. [See a demo of how it works here.](https://youtu.be/67HN81nYD4Q?si=r216nZ7HTsp8l-Sp) Once your PDF data is imported into a Parabola Flow, you can create powerful workflows to combine it with data that lives in other tools, then transform, visualize, and act on it—and it’s all automated.‍ ## How customers convert data from PDFs to spreadsheets Customers who have leveraged Parabola for [PDF parsing](https://parabola.io/blog/parsing-pdfs-with-parabola) are realizing incredible benefits for different use cases—[from catching every freight discrepancy to recoup millions annually](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets), [to saving time digitizing Bill of Lading details every day](https://parabola.io/tool/how-to-use-ai-to-convert-data-from-a-bill-of-lading-to-a-spreadsheet). In addition to error-proofing data and saving time, customers have shared that they’re able to process more data without adding headcount, and their junior employees have become more engaged because they’re no longer manually inputting data. Here are just a few of the doc types customers are using our PDF parsing features to manage every day: - [Commercial invoices](https://parabola.io/tool/use-ai-to-convert-data-from-commercial-invoice-pdfs-to-spreadsheets) - [Bills of Lading](https://parabola.io/tool/how-to-use-ai-to-convert-data-from-a-bill-of-lading-to-a-spreadsheet) - [Freight invoices](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets) - [Purchase orders](https://parabola.io/tool/use-ai-to-convert-purchase-order-pdfs-to-spreadsheets) - [Packing lists](https://parabola.io/tool/use-ai-to-convert-data-from-packing-lists-to-spreadsheets) - [CBP Form 7501](https://parabola.io/tool/use-ai-to-convert-data-from-a-cbp-form-7501-to-a-spreadsheet) So the next time you catch yourself manually copy-and-pasting data from a PDF into a spreadsheet, [remember there’s a better way](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet). Stop sitting on your data and start acting on it with Parabola. Want to learn more? [Click here](https://parabola.io/api/clipboard/53ae8ae2-19c2-49d3-93f4-8b3c431c0f71/copy_to_flow?name=PDF+Parsing+with+AI) to try it this feature out for yourself. --- # Automating Inventory Reconciliation and Reporting: A Complete Guide Source: https://parabola.io/blog/inventory-reconciliation-and-reporting We all know the pain of a [stockout](https://fiixsoftware.com/glossary/what-is-stockout/#:~:text=A%20stockout%20is%20a%20situation,difficult%20to%20predict%20and%20prevent.): you’ve been craving that particular mac n’ cheese, or you need a re-up on your favorite socks. But when you get to the store, the shelf is empty, or the site is out of stock.If you had to guess, how costly are stockouts: A few hundred million? Billions? The percentage of desired items that end up being out of stock is [estimated at 8%](https://www.netsuite.com/portal/resource/articles/inventory-management/stockout.shtml). That means that almost one shopping experience out of ten ends in disappointment, and the cost to retail businesses is on the order of [nearly a trillion dollars a year](https://www.sostocked.com/stockout/#:~:text=Stockouts%20are%20one%20of%20the,1%20in%203%20shopping%20trips.). There’s a simple solution — often time-consuming, but always necessary: [inventory reconciliation](https://parabola.io/use-cases/inventory-reconciliation). Let’s walk through what that process entails, why it can be so painful, and how it might be streamlined. ## **What is inventory reconciliation?** Inventory reconciliation is a chance to play detective by comparing your recorded inventory to your [inventory on hand](https://parabola.io/use-cases/inventory-reconciliation). This process might include a physical count, but can also focus solely on the different platforms that track inventory, sales, and financial planning. [Use Parabola to automate inventory reconciliation. Learn more here.](https://parabola.io/use-cases/inventory-reconciliation) Inventory reconciliation (and inventory management reporting) have one basic goal: to [identify discrepancies](https://parabola.io/use-cases/inventory-reconciliation). Are your documents all telling the same story, or are they full of holes? Using a warehouse management system (WMS) or another source of truth, you can ensure that both customer-facing and internal counts are up to date. ## **Why is inventory reconciliation important for ecommerce and retail companies?** Beyond fixing discrepancies, inventory reconciliation gives a window into the supply chain process. Behind every number is a chance to dig into why discrepancies occur, and to take steps towards fixing them. For example, you might identify that your warehouse team needs better training, or that your procurement team is lacking specific software tools needed to attain products in a timely manner. Operations and supply chain processes can be huge growth levers for ecommerce and retail companies, but that requires visibility into what’s working, and what’s not. Consistent, careful inventory reconciliation can be an important way to get that visibility. ## **Why is inventory reconciliation so challenging?** As crucial as it is, reconciling your inventory is tedious. It typically requires a lot of time and effort, thanks to the vast amounts of data involved. And the more retail and fulfillment partners are involved, the more expansive and messy the data can get. #### **Data volume** It’s not just warehouses: Inventory reconciliation surveys ingoing, outgoing, and [in-transit inventory](https://parabola.io/blog/track-in-transit-inventory). A brand might have hundreds or thousands of unique product IDs (SKUs) so you can take that already sizable number and effectively triple it. With scores of products moving in and out, your warehouses need to be tidy, and your data does too. If your inventory is not kept up-to-date, it’ll be impossible to have a clear view of what’s actually in stock. #### **Lack of data standardization** Even with a standard operating procedure ([SOP](https://parabola.io/resources/the-sop-community)) in place, all of your partners are going to relay information to you in different formats, on different cadences. This will make your reconciliation process more error-prone. On a base level, it can be needlessly hard to recognize if you have all the data you need, in its most up-to-date form. Even once you have that data, the manual effort needed to format, organize, and analyze takes time — and every step increases the possibility for manual error. ## **How often should you reconcile your inventory?** The short answer: As much as possible. The longer answer is that, given your needs and capacity, your best solution might not look like someone else’s. Different periodic intervals make sense for different kinds of business. If stock moves seasonally (swimsuits, for example), inventory reconciliation might happen in anticipation of peak periods. If a brand sells more consistently throughout the year, they’re probably on a set schedule that doesn’t correlate to order volume. No matter how often you choose to reconcile inventory, you should ensure that the process is happening as consistently as you can manage — and that it’s done proactively, not reactively. ## **Simplify inventory reconciliation and reporting with Parabola** Simplifying inventory reconciliation can support supply chain leaders’ goals in more way than one. Overall, it will help reduce errors, make supply chain operations more predictable, and cater to continuous, consistent improvements. Although the inventory reconciliation process has a bad rap for being time-consuming and painful, it doesn’t have to be. Tools like Parabola can support you across every part of that journey. Here’s a quick summary of how to improve your inventory management tracking: #### **1. Automate inventory reconciliation** If you’re able to automate your inventory reconciliation process, then it becomes an “always-on” function. Parabola enables you to create automated workflows for inventory reconciliation, helping you identify mismatched records without manual effort. If you also take time to research and invest in inventory and [warehouse management systems](https://parabola.io/the-supply-chain-tech-stack-report-lp), you can build out and [automate your inventory reporting](https://parabola.io/blog/automated-inventory-reporting)—from ABC reporting, to days on hand reports, consolidations, reconciliations, and more. You’ll have real-time visibility into performance across the supply chain, inventory records, and with ease, be able to compare that to what you have on-hand. **This is especially the case if you’re dealing with**[multiple 3PLs](https://parabola.io/blog/3pl-management)**or warehouses.** Without automation, more warehouses means greater data volumes and less structure, which will only create more manual work, and ultimately, more discrepancies. #### **2. Invest in advanced tracking technology** It’s also important to streamline manual labor. Equip your warehouse team(s) with [barcode or RFID systems](https://www.sortly.com/blog/rfid-vs-barcode-for-inventory-management/) that allow for simpler, more efficient tracking of inventory movement. By setting your team up with a clean, digital scanning process, not only do their lives become easier, but your data capture process will be much more streamlined, and more free of human error. Generally, these systems allow for real-time data updates as items are scanned and stocked (or put into movement). This is a crucial piece of the data-puzzle. If this data is properly fed into your IMS/WMS/workflow tools, you’re essentially relaying inventory data directly from a scanner to a report or dashboard, allowing for more consistent, more accurate reconciliations. #### **3. Centralize your data systems** As an extension of automation, you should also [centralize your systems as much as possible](https://parabola.io/product/integrations), to give yourself a single source of truth to access and maintain inventory records: Import data from all your systems into Parabola, whether it’s an order and inventory management system or spreadsheets. By consolidating your information, you can easily track discrepancies and ensure accuracy. **Ultimately, centralized systems connect all of your data to all of your teams at all times.** Centralizing your inventory data will give all of your organization’s teams (supply chain or otherwise) visibility into inventory operations and performance. This will help streamline recording and reporting, and foster better communication between your team internally, and between you, vendors, and suppliers. With better visibility and communication, you’ll be able to tackle supply issues more efficiently, and have a deeper viewpoint into what needs to be reconciled, and what the root cause of any disruptions were. #### **4. Integrate an inventory management and tracking system** Pair Parabola with your existing[inventory management](https://parabola.io/use-cases/inventory-reconciliation) and tracking system to enhance visibility across your supply chain. Whether you’re dealing with warehouses or retail locations, you’ll have a unified view of your operations. This is key to centralizing your data and getting all of the benefits outlined above. #### **5. Refine your demand planning and forecasting methods** Forecasting and reconciliation are different sides of the same coin. More accurate planning leads to less work later—with less whiplash, and less need for reactive behavior, reconciliations likely won’t require as much lift. By automating your [demand planning and forecasting](https://parabola.io/) and turning them into “always-on” functions, reconciliations should play out much easier. Over time, your reconciliations and demand planning can actually work in tandem, *with* one another, instead being at odds. #### **6. Track key metrics with custom reports and run root cause analyses** Assuming you have the right tools in place, and therefore real-time visibility into the entire supply chain, you can also track problems down to the source. And running a [root cause analysis](https://www.linkedin.com/advice/3/what-best-way-conduct-root-cause-analysis) allows you to get down to the exact point where a hold up, error, or discrepancy occurred. It’s what allows you to identify the “why” behind any issue. With a tool like Parabola, you can build tailored dashboards for inventory management reporting to monitor stock levels, fulfillment accuracy, and trends. With real-time updates, you can make better decisions faster. Tracking down to the roots also helps you remedy the workflow itself, not just the disruption that took place. It’s what helps you make fixes that are permanent. Run root cause analyses to find the weak links in both ingoing and outgoing inventory processes. ## **How to automate inventory reconciliation** Workflow tools like Parabola stand to benefit you across all [inventory management](https://parabola.io/use-cases/inventory-reconciliation) operations—especially with automating and standardizing your inventory reconciliation process. It can essentially act as your inventory management tracking system. Parabola makes supply chain management a much less laborious process, helping you centralize workflows, automate reporting, and set detailed [SOPs](https://parabola.io/resources/the-sop-community) for each of your workflows. Whatever the case or the technology, however, the most important part is to continually optimize your inventory reconciliation process, as it stands to make a great impact on your delivery times, and drive consistently positive experiences for consumers. --- # How to Parse PDFs Effectively: Tools, Methods & Use Cases [Updated for 2026] Source: https://parabola.io/blog/best-methods-pdf-parsing The portable document format — ubiquitously referred to as the [PDF](https://www.adobe.com/acrobat/about-adobe-pdf.html) — has long been the gold standard for digital document sharing: customizable, universal, and compact. But the very qualities that make the filetype so useful can also make PDFs rigid and hard to interface with. [Turn messy data from PDFs and invoices into structured datasets using AI.](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet) Think of PDFs as artisanal glass vaults: They're excellent at preserving documents according to the maker’s intention, but it can be challenging to get inside without breaking stuff. For workflows that utilize PDFs, this means a lot of valuable data stays locked inside, just out of reach. To get a little more insight, we spoke with some members of Parabola’s Product, Engineering, and GTM teams about PDF parsers: Here are the use cases, challenges, and best practices you need to know if you work with PDFs. But first, let’s cover the basics. ## PDF parsing: a TL;DR [PDF parsing](https://web.archive.org/web/20250618213618//blog/best-methods-pdf-parsing) is the process of extracting text, images, and or any other data from a [PDF file](https://web.archive.org/web/20250618213618/https://www.adobe.com/acrobat/about-adobe-pdf.html). From a high level, the process of parsing includes analyzing and identifying specific elements throughout a file, and then pulling out those specific elements. Beyond text and images, that might also include fonts, layouts, tables, and even metadata. ## PDF parsers; a TL;DR PDF parsing is used by professionals [across many industries](https://web.archive.org/web/20250618213618/https://nanonets.com/blog/pdf-parser/), most generally to pull information from one document, to then repurpose and use more specifically in another place. In many cases, that means pulling information from a PDF to input into an Excel file to manipulate as part of a dataset, to be used for specific workflows. For freight, ops, and logistics professionals, a PDF parser streamlines the process of digitizing and organizing PDF data, making it easier to manage shipments, track costs, and perform analysis. ## What is PDF parsing used for? **Example workflows that utilize PDF parsing:** - [Invoice automation](https://parabola.io/use-cases/accounts-receivable-aging-report)**:** Invoice number, date, items purchased, and payment amounts can be extracted to automate invoice processing and payments. - [Purchase order and receipt processing](https://parabola.io/tool/use-ai-to-convert-purchase-order-pdfs-to-spreadsheets)**:** Refunds and reimbursements can be automated by parsing items, dollar amounts, dates, etc. - **Legal, medical, governmental records analysis:** Any in-depth analysis that requires the identification and/or extraction of names, dates, citations, dollar amounts, medications, and more, all make great use of parsing. - **Financial and insurance processing:** Similar to analysis, PDF parsing is a very commonly used by companies assessing risk and analyzing balance sheets. - **Survey/form analysis:** Text extraction is very helpful to pull responses and collect information from forms and surveys. - **Resume extraction:** Parsing makes it simple for recruiters to filter and analyze resumes based on candidate details, contact information, work experience, and more. Essentially any type of reporting, analysis, or archiving could require use of a PDF parser at one point or another. The challenges that can arise from PDF parsing, however, typically surface when it’s needed to be done at scale. ## Challenges of PDF parsing There are many [benefits of using PDFs](https://www.adobe.com/acrobat/hub/why-pdf-is-best-format-for-business.html). PDFs are secure. They’re compatible with any device. They compress files to very convenient sizes. They’re easy to scan, and ideal for printing. There’s a reason why they’re used for so many essential business documents and processes. The discourse around PDFs, however, has also always been about how difficult it is to extract and translate information from them. *PDFs are limiting. The same characteristics that make them great are why they’re complicated to work with when digitizing documents.* 1. **PDFs are a bit rigid in nature.** While that *is* what contributes to the format’s consistency, it’s also what makes them harder to manipulate. 1. **Unstructured data** presents a major roadblock to being able to quickly analyze the contents of a file and extract needed information. The main challenge for most parsing software is that, much like the paper documents they aim to mirror, PDF formats can vary widely. Though PDF parsers may offer consistency, they tend to lack flexibility. Parabola engineer Jordan Lawler notes that in order to process a particular document type quickly and accurately, some parsers need to be trained on hundreds of instances of the same document. This requires great upfront time investment, which can be virtually undone by small changes to the source document. Plus, even using a flawless PDF parser can be a needlessly manual process, says Adam Reisfield, Special Projects Lead at Parabola: “Today, realistically, I would just give the PDF to ChatGPT, but the limitation there is that’s not an operationalized part of my process. Next time I receive a PDF, I’m gonna have to open up ChatGPT, drop that PDF in there — it’s not actually helping me automate that whole end-to-end process.” For all of these reasons, although PDF parsers are widely useful, they can be brittle and hard to apply at scale, particularly for a process like a [freight audit](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets) or a complex logistics workflow, which might involve thousands of PDFs using a range of layouts and formats. ## When and how to use a PDF parser Brian Sanchez, Product Manager at Parabola, emphasizes that “the best PDF parser is a person.” People are great at using context to decipher unclear data, and can quickly integrate new learnings. That said, a repetitive process like data entry can be error-prone, and securing headcount to sift through data manually comes at a cost to the business. A competent PDF parser can take something that might be a highly manual process (scanning a table and copying values to another document piece by piece), and make it near-instantaneous. Here are some use cases where PDF parsing can make a difference: #### Parcel invoice audit Billing discrepancies might be small on an individual invoice, but differences in shipping costs can quickly add up. A [parcel invoice audit](https://parabola.io/use-cases/parcel-invoice-audit) is a meticulous process, often involving many different document types, but a PDF parser can deliver value quickly by pulling out only the most relevant data. #### Inventory reconciliation Particularly during busy shopping seasons, maintaining accurate inventory is a challenging but critical business operation. [Inventory reconciliation](https://parabola.io/use-cases/inventory-reconciliation) typically involves pulling real-time figures from several sources (sometimes in messy formats) — exactly the kind of task for which a PDF parser is well-suited. #### Order management A platform like Shopify delivers order information in consistent templates, but every new channel can mean another structure to show cost of goods sold (COGS), as well as variable shipping costs. Using a PDF parser to [process orders](https://parabola.io/use-cases/inventory-reconciliation) enables you to work across documents and bring your business data into a consistent, readable form. ## Parse data from a PDF: five PDF parsing tools Think of PDF parsing tools as keys to your digital vault. The right key depends on your specific needs — whether you're handling a few documents or processing thousands; working with simple forms or complex layouts. #### 1. Online converters and parsers **‍**Tools like Zamzar and Smallpdf are quick and convenient for PDF parsing. They’re perfect for occasional use but can struggle with accuracy and complex layouts, making them less reliable for intricate tasks. #### 2. Adobe acrobat **‍**Adobe Acrobat is a great PDF parser, especially for those looking to maintain formatting like tables and images. However, if you’re working with highly detailed files—like purchase orders—you might still need some manual fine-tuning. #### 3. Manual copying and pasting **‍**For full control, you can copy and paste data directly. While this works for one-off tasks, it’s tedious and prone to mistakes, especially when parsing PDFs with large volumes of data. #### 4. Shakudo ExtractFlow For organizations with strict security or compliance needs, [Shakudo ExtractFlow](https://www.shakudo.io/extractflow) enables secure AI data extraction from various formats (PDF, DOCX, TIFF) that runs entirely within the customer's private cloud. It ensures state-of-the-art accuracy by continuously providing access to the latest OCR and LLM models without requiring any engineering overhead. #### 5. Parabola’s PDF parser **‍**For a scalable and efficient way to parse PDFs, Parabola's advanced tool combines Optical Character Recognition (OCR) with Large Language Models (LLMs). It lets you[extract](https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-pdf-data) and organize data with ease, guided by natural language prompts. #### What’s the best fit? **‍**For straightforward tasks like converting [PDFs to Excel](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet), online tools or Adobe Acrobat may suffice. But if you’re managing high volumes or handling complex formats like detailed purchase orders or freight invoices, Parabola is the smarter choice for precision and efficiency.With the right method, parsing PDFs can go from a chore to a streamlined part of your process. ## How to parse a PDF file with Parabola Fortunately for those working in operations and logistics today, advancements in AI have made tooling for PDF processing far more robust than previous ineffective or high-code approaches. The advantage of a tool like Parabola is that it is able to ingest PDFs at scale, and with a great degree of customization and accessibility. The key lies in combining optical character recognition (OCR) vision technology with state-of-the-art large language models (LLMs). On the front end, says Reisfield, you don’t need any technical knowledge, just the ability to tell Parabola in natural language what type of document it’s looking at, and how you want the tool to read it. Equipped with this natural language prompt, Parabola uses LLMs on the backend to interpret the user request, and applies OCR tech to read the document as a human would. According to Sanchez, this means that Parabola can be “as flexible as a person reading each PDF individually, but as precise and efficient as a computer parsing massive quantities of documents automatically.” And not only does this technology excel at extracting data quickly and accurately, Parabola is also uniquely positioned to help you get that data where it needs to go. Says Reisfield, “Even the tools that are the best in the world at pulling information off of PDFs — they’re almost never also best-in-class logic builders that have integrations with every tool in your stack.” ### Five steps to use AI to convert data from a PDF to a spreadsheet using Parabola Here's how to use AI to convert data from a PDF to a spreadsheet in five simple steps, using Parabola: 1. Set up your data source by creating a new Parabola flow and uploading your PDF files. This creates your workflow foundation. 1. Use Parabola's AI document processing tools to analyze and identify key information. Configure any necessary text recognition settings. 1. Apply AI extraction rules to pull specific data points and content sections. This step converts unstructured PDF data into organized format. 1. Use Parabola's spreadsheet formatting tools to structure the extracted data into your desired layout. Add any necessary data cleaning or validation. 1. Generate your results by previewing the converted data and running your automated flow. Once configured, this process will handle new PDFs automatically. ## Choosing your path forward Think of PDF parsing tools as keys to your digital vault. The right key depends on your specific needs — whether you're handling a few documents or processing thousands; working with simple forms or complex layouts. By matching your requirements to the appropriate solution, you can transform PDF processing from a bottleneck into a streamlined part of your workflow. Consider your organization's use cases, volume requirements, and technical resources when choosing between lightweight online converters, or automated solutions with built-in integrations. The right choice will unlock not just your PDFs, but your team's productivity as well._____________________________________ ## PDF parsing FAQs ### What is PDF parsing? PDF parsing is the process of extracting text, tables, images, metadata or other structured information from a PDF document and converting it into a machine-readable format like a spreadsheet or database. ### Why is PDF parsing important for operations, logistics and finance? Because many critical business documents (invoices, packing lists, audit sheets, shipping manifests) are delivered as PDFs, parsing them enables teams to digitize, automate and analyze that data rather than manually transcribing it. ### What are the main challenges of parsing PDFs? Challenges include wide layout variation across documents, scanned/image-based PDFs requiring OCR, unstructured data (e.g., free-form text or non-tabular layouts), and maintaining parsing accuracy at scale when formats change over time. ### What methods or tools exist to parse PDFs? Methods include: - One-off online converters or tools for simple PDFs - Traditional PDF parsers for consistent digital layouts - AI-powered solutions combining OCR and large-language-models (LLMs) for complex/unstructured documents ### When should I use an AI-powered PDF parsing workflow? Use it when you’re processing many PDFs, dealing with frequent layout changes, need to extract from unstructured or variable formats (e.g., invoices from many vendors), and need automation rather than one-off manual work. ### How does Parabola’s PDF parser differ from typical tools? Parabola combines OCR + computer-vision-enabled LLMs, handles layout variations, enables you to tell the system in natural language what to extract, and integrates the parsed data into end-to-end workflows (not just extraction). ### Can I automate the parsing of PDFs so the process repeats without manual intervention? Yes — by building workflows that ingest PDFs (e.g., via email or file upload), extract data, apply transformations or logic, and export or load into other systems. Parabola supports batching and scheduling of flows. ### What kinds of documents are well-suited to PDF parsing? Documents such as freight invoices, packing lists, purchase orders, bills of lading, commercial invoices, survey forms, receipts—all of which commonly exist as PDFs and contain structured or semi-structured data. ### How do I maintain parsing accuracy when document layouts keep changing? Best practice: use a tool that adapts to variations; configure extraction using keys and tables (not assume perfect uniformity); provide directives or examples to the AI; monitor results; refine rules over time. ### What is the ROI of moving from manual PDF-data entry to automated parsing? You’ll save significant hours of manual work, reduce human error, scale processing of documents, make data actionable faster, and free your team to focus on higher-value tasks rather than copying and pasting. --- # Bill of Materials (BOM): What You Need to Know – and Automate Source: https://parabola.io/blog/bill-of-materials The bill of materials (BOM) has become a foundational piece for supply chain managers and operations directors trying to drive efficient production and manufacturing. A bill of materials is typically relied on to facilitate efficient procurement, production, and inventory and supply chain management. Below, we’ll dive into what a bill of materials is (with examples), how it works, challenges, and how you can automate the BOM process. ## **What is a bill of materials?** A [bill of materials (BOM)](https://www.investopedia.com/terms/b/bill-of-materials.asp) is a comprehensive document that highlights a detailed roadmap of what is needed, and what needs to be done to manufacture a product. BOMs often include exhaustive specifications, including: A well put together bill of materials requires meticulous attention to detail. When done right, however, a BOM serves as a linchpin in the manufacturing lifecycle of any individual product. ## **What makes BOMs important?** The impact of a bill of materials can be felt across the entire [supply chain](https://parabola.io/use-cases/inventory-reconciliation). A BOM is instrumental in optimizing operational efficiencies and ensuring product accuracy and consistency. This helps you better control costs and stay on efficient schedules. By understanding procurement and production timing, you can better plan and forecast the purchase of raw materials and other foundational components. This gives you a cleaner window into cost estimations. You can also optimize the *quantity* of materials needed, effectively reducing production waste. When the manufacturing process is delayed or fails, a bill of materials serves as a record of production, making it easier to backtrack and identify the exact point in which the supply chain was broken. ## **Bill of materials impact on cost of goods sold** A bill of materials is also an essential piece in calculating cost of goods sold (COGS). [Cost of goods sold](https://www.nerdwallet.com/article/small-business/cost-of-goods-sold) includes only costs *directly* related to procuring and making products. That entails raw material cost, manufacturing cost, packaging, shipping and freight, and the related labor costs. In other words, it’s your BOM, plus labor and shipping costs per unit of inventory. While the lines can be blurred depending on the exact service or product being offered, generally, COGS will not include tangential costs like marketing, sales, distribution, and other administrative expenses. **Calculating COGS can be done using the following formula:** COGS = Beginning Inventory Cost + Purchased Inventory Costs - Ending Inventory Accurately calculating cost of goods sold is critical for knowing the true cost of producing your merchandise. BOMs serve as a critical factor in calculating COGS, setting product prices, and forecasting potential spend and revenue. ## **Who benefits most from using BOMs?** The companies that typically benefit most from using BOMs are those that need to keep a close eye on the cost of manufacturing a large inventory of goods. Why? It’s typically more relevant and doable for larger companies to rely on BOMs, given the volume at which they manufacture products, and the bandwidth required to calculate, understand, and draw insights from COGS, profit margins, and other revenue impact factors. Within these companies, supply chain managers, procurement, and manufacturing teams are the main teams that stand to benefit from a bill of materials. That said, various departments and stakeholders across the supply chain get use from BOMs. **Engineering and design teams** utilize BOMs (or [EBOMs](https://www.greenlight.guru/blog/ebom-vs-mbom)) to ensure that designs align with the availability of materials and required timing of production. **Quality control teams** refer to the BOM to verify that the right components are used in the right order throughout the production process. In some cases, especially for more complex or customizable products, **sales and marketing teams** may actually refer to a BOM to understand intricacies and nuances they may want to relay to customers. A bill of materials is also a useful document for any stakeholding decision-maker. Board members, investors, and internal management can use information present in BOMs to understand supply chain performance and inform go-forward strategy. ## **Bill of Materials Example** As an example, here’s a stripped-down version of what a bill of materials might look like for a chair: ## **Bill of materials templates** A quick web search allows you to find a variety of different BOM templates. Depending on your need, you can also specify the kind of template you need, whether that’s for Excel, Google Sheets, etc. A few websites currently offering bill of materials templates are: ## **Bill of materials management challenges** Assembling and managing a bill of materials can be challenging due to the volume of data required, and the disparate sources in which that data comes from. Without the right software and processes in place, it can become incredibly time-consuming to manage BOMs across your entire inventory. #### **Lack of data standards** Each product is made up of a multitude of different components. Each of those components likely comes from a different supplier, on different timelines, with different units of measurement, and different overall data standards. That can create an immense body of work for your team to [standardize](https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data) all of that data across each individual product. Additionally, since this information is coming from so many disparate locations, it can be difficult to centralize all of that into one place. Let’s refer back to our chair as an example… Above, the bill of materials example may look fairly simple and straightforward. Broken down by its components, however, you can see how disparate the data would be. To get to the final example above, you’d have to centralize and [standardize all data](https://parabola.io/transform/standardize-with-ai) from your wood supplier, metal supplier, fabric, paint, and so on. #### **Volume and granularity of information** On top of that, the *amount* of information needed to assemble BOMs is quite large. Sourcing teams, for example, may need to manage a bill of materials that ties back to the SKU of each individual product, inclusive of all relevant manufacturing data. While brick and mortar stores may only have a few thousand SKUs on-hand, e-commerce companies may have *hundreds of thousands of SKUs*. It goes without saying, but managing this many BOMs is a massive feat. #### **Manual, time-consuming processes** Most companies rely on Excel to manage their BOMs. The problem is that there’s no inherent way within Excel to automate the centralization and standardization of all the involved data. Unless you have a developer on-hand building [automations via VBA](https://parabola.io/blog), you’re typically required to copy and paste data from all of your vendors and manufacturing partners into one place, and format that data yourself. Not only is this time-consuming, but it’s likely (and natural) to be error-prone. ## How to simplify the BOM management process Managing a bill of materials is essential for efficient production and operations. With Parabola, you can streamline and optimize the BOM management process to save time and reduce errors. Here’s how:**‍** #### 1. Streamline the bill of materials management process Parabola simplifies workflows for bill of materials management, allowing you to integrate data from multiple sources. This ensures your BOM stays accurate and up-to-date, reducing inefficiencies across your production cycle. #### 2. Enhance operations with BOM software Pair Parabola with your existing bill of materials software to automate tasks like data consolidation and reporting. With Parabola, you can take your BOM management to the next level by building custom, automated workflows tailored to your needs. #### 3. Track and improve the BOM management process Gain real-time insights into your bill of materials in supply chain management with [Parabola’s automation capabilitie](https://parabola.io/product/overview/introduction-to-parabola)s. By identifying discrepancies and tracking changes, you can make smarter, data-driven decisions. #### 4. Visualize your bill of materials example in action Parabola helps you create and analyze a bill of materials example with ease. Whether it’s a single product or a complex assembly, you can map out every component to ensure your operations run smoothly. #### 5. Optimize your supply chain with better BOM management By automating your bill of material management process, Parabola helps you reduce delays and improve collaboration. From small businesses to large enterprises, an efficient BOM management process is key to supply chain success. [With Parabola](https://parabola.io/), managing a bill of materials becomes simple, efficient, and scalable. Whether you’re using advanced BOM software or need a flexible tool for automation, Parabola empowers you to streamline your bill of materials management process and focus on what matters most: delivering exceptional products. --- # What Inventory Reports Should You Automate? Source: https://parabola.io/blog/automated-inventory-reporting Each day, inventory managers carry out the balancing act of ensuring products are readily available to meet customer demand while keeping excess stock to a minimum. At the heart of this equilibrium lies the need for accurate and timely inventory reporting. As any inventory manager knows, however, inventory reporting can be a painstakingly manual process. Despite being a strategic and operational necessity that stands to inform company decisions and direction, inventory reporting often gets stuck within the confines of spreadsheets. [Reliance on manual spreadsheet reporting](https://parabola.io/blog/how-to-automate-excel) lends itself to a number of drawbacks aside from just time decay. The good news, however, is that most inventory reports [can be automated](https://parabola.io/solutions/operations). Here, we’ll look at the different inventory reports you can and should be automating (and why you should do so), including inventory days on hand, visibility reporting, and more. ### Why is inventory reporting important? From a general perspective, inventory reporting brings you heightened accuracy and efficiency in how you operate *today*, while providing you with a world of insights to inform how you operate *tomorrow*. Inventory reporting gives you timely insights into what’s on hand, how long it’s been there, what’s incoming, what’s outgoing, and the operational efficiencies around each of those areas. With those insights, you’ll be able to: Overall, you will be able to better understand your efficiencies and deficiencies, reduce costs, and better set up your business to scale. #### What gets in the way of consistent, accurate inventory reporting? Timeliness and human error are the two biggest culprits here. Relying on manual data entry across ordering, storing, and shipping each individual product in your warehouse opens up a multitude of different points for human error to occur and makes it inevitable. By requiring human attention for data to be retrieved, logged, or updated, consistency is hard to come by, and you’ll not be operating at the speed you could be with an automated inventory system. ### Why automate inventory reporting? What are the precise reasons to automate your [inventory days on hand](https://parabola.io/use-cases/inventory-reconciliation) and other reports? 1. **Accuracy of data.** This comes from making every effort to minimize human error.2. **Getting time back to do more business-critical tasks.** By saving hours previously spent in spreadsheets, you’ll be able to spend more time on planning, forecasting, and making improvements to your inventory operations. 3. **Standardizing your data.** With more predictable, consistent workflows in place, you’ll be able to set stricter standards and operating practices for how your data is organized, recorded, and reported on. 4. **Creating a single source of truth.** Automation requires most, if not all of your systems to be [connected digitally](https://parabola.io/product/integrations). What you also get from this is the ability to funnel all of your data into a single, centralized source of truth.This will allow your team to manage data and collaborate all in one place, ensuring everyone in the company is operating on the same page, and that inventory data is more easily integrated into shipping, accounting, or other necessary business functions. Above all else, automated inventory systems are set up to be scalable. So as your operations grow, you’ll still be able to spend time on business-critical work and trust in the accuracy of your data reporting. ### Inventory reports to automate Below we’ll break down the different [types of inventory reports](https://www.extensiv.com/blog/inventory-reports) you should automate and the reasons why you should do so, starting with inventory days on hand reporting. #### Inventory days on hand Inventory days on hand is a measurement of how long it takes to sell through your entire stock of inventory — it quantifies how many days you’ll be able to sustain your current sales rate with what’s available in inventory. Inventory days on hand is one of the more critical pieces of information to keep track of to maintain efficient inventory management practices. The inventory days on hand formula is: > Average inventory x Cost of goods sold (COGS) x 365 Average inventory is the average dollar amount of inventory you have over your entire accounting period (which in this example is one year). You can calculate that by adding your starting inventory number and your final inventory number, and dividing that by two. If you divide that number by your total cost of goods sold and multiply by the number of days in your accounting period, you’ll have your number of days of inventory on hand. For example, if your average inventory is $625,000, and your cost of goods sold was $3.2 million, you’d have about 71 [days of inventory on hand](https://parabola.io/use-cases/inventory-reconciliation). There are a few different [days on hand calculators](https://www.wallstreetprep.com/knowledge/inventory-days/) you can refer to online to get a sense of how it can be measured. ##### Challenges of automating inventory days on hand reporting There’s a lot of data required in automating your inventory days on hand report, so it can take quite the effort to maintain both accurate and up-to-date information. Then, all of that information needs to be aggregated and combined — parsing PDFs, extracting data, and updating inventory spreadsheets in real time can become very time consuming. If your [inventory DOH](https://parabola.io/use-cases/inventory-reconciliation) is low, that’s an indication that you’re operating efficiently — not overstocking and turning over inventory quickly. Low DOH also means liquidity. Less inventory on hand means more cash on hand, which can be used more flexibly to invest in other areas of the company. Lastly, a lower DOH number also means less risk. With less cash tied up in your inventory, you can better mitigate risks in any situations where specific products lose their demand or have other issues. By automating inventory days on hand reporting, you’ll have real-time visibility into whether you’re overstocking or understocking specific items. You’ll be able to automatically parse PDFs, integrate data on existing inventory and sales, and feed all of that into a dashboard for up-to-date status checks. All of this information in place leads to increased adaptability and better forecasting. You can react more immediately in the short term to avoid having too much cash tied up into your inventory. You’ll also have more in-depth insights readily available to help you forecast demand and plan for future accounting periods. #### Inventory consolidation and visibility An [inventory consolidation and visibility report](https://parabola.io/use-cases/consolidated-inventory-reporting) is an overall comprehensive picture of the status, movement, and availability of all of your inventory, to be used to make sure you’re operating in the most optimal way possible. [Inventory visibility](https://cogsy.com/inventory-management/inventory-visibility/) is a holistic view of your entire inventory, showing what is sitting and available, what is in motion, and so on. It’s the ability to measure inventory levels in real-time. Consolidation reports expand into the full supply chain, taking a look at inventory, but also vendors, ordering, and shipments, to see where consolidations can be made to save time and money. ##### Challenges of inventory consolidation and visibility Reporting The challenges here are largely similar to that of DOH reporting. Aggregating all of this data typically takes a lot of time and effort. Keeping it up to date and managing that data takes even more time. Automating inventory consolidation reporting will allow you to automatically and regularly update your spreadsheets to include the most recent, accurate inventory data to ensure efficiency, cost savings, and improves decision-making and forecasting. #### Backorder reporting Backorder reporting shows the number of items that were unavailable to be shipped at the time of order, and thus backordered. Within this report, you’ll see detailed reporting of what happened, including the reason for backorder, the customer, and a whole line of other documentation. ##### Challenges of backorder reporting The biggest challenge here is timeliness in both identifying items that have been backordered and quickly fulfilling those orders, and also in managing communications around backordered items. By automating your backorder reporting process, you’ll have immediate updates and awareness when items have been backordered. You can automate notifications as well as triggers for communications and even automatically send emails or messages upon an item being deemed backordered. Automations can help you gather more in-depth data to avoid order backlogs and prevent future stockouts. #### Bundle explosion reporting Bundle explosion reporting is the process of “exploding” — in other words, *isolating*— bundled inventory items into individual items for the sake of tracking their status and history. ##### Challenges of bundle explosion reporting You may have [an array of products](https://parabola.io/blog) under a single bundle. The act of isolating all of those items takes a lot of time and manual work. Not only are individual items different, but bundles themselves are also constructed differently, and with varying levels of complexity, and dynamic, variable pricing. By automating bundle explosion, you can speed up the reporting process for any number of dynamic item configurations. As bundles grow or rotate, it’ll be much easier to update and scale your reporting. #### Inventory reconciliation Inventory reconciliation is the process of comparing and aligning your actual inventory on hand with what is recorded within your [inventory management systems](https://katanamrp.com/blog/automated-inventory-management/). Like most types of inventory reporting, all of the manual work that is required here is a major challenge. It often takes hours to pull in the data that your warehouses or 3PLs provide because it's all in spreadsheets in different formats. And if you're dealing with multi-warehouse [inventory reconciliation](https://parabola.io/blog/inventory-reconciliation-and-reporting), the time it takes to manually manipulate and manage all this data is magnified — not to mention the room for error that comes with even more manual data entry. If your [inventory reconciliation report](https://parabola.io/use-cases/inventory-reconciliation) is automated, you’ll always have recorded data and [inventory on hand](https://parabola.io/use-cases/inventory-reconciliation) numbers readily available. Instead of needing to tally up these numbers in real-time, they’ll be sitting there awaiting your validation. #### Inventory burndown Inventory burndown is the process of tracking the time-lapse depletion of your inventory levels over a specific period. It essentially tracks the speed of inventory being reduced, so you can project timelines to when your inventory will be fully depleted. ##### Challenges of Inventory Burndown Tracking burndown across all of your individual items over varying time periods (daily, weekly, monthly) can be extremely time-consuming when done manually. Additionally, burndown reports are best [presented visually](https://parabola.io/updates), typically in line graphs. Between different chronologies and product lines, this becomes a project of its own, and can be difficult to set up in Excel. By automating inventory burndown, you’ll have an always-on burndown tracker to monitor real-time inventory levels and continuously update graphs as needed. It ensures accuracy and speed across any level of nuanced data. #### ABC inventory ABC inventory reporting is a method of categorizing and prioritizing inventory items based on their significance or importance, measured in value generated. The idea is to classify inventory items as Class A, B, C, and so on. Class A items are the most significant. [As Netsuite highlights](https://www.netsuite.com/portal/resource/articles/inventory-management/abc-inventory-analysis.shtml), these items can be looked at in the vein of Pareto’s 80/20 principle—they’ll likely make up around 20% of your stock, but about 80% of your sales/value. Class A items are those that drive the most value, but don’t typically require a large effort or cost to dispense. ##### Challenges of ABC inventory reporting Extracting data across sales and stock numbers—whether from PDFs or spreadsheets—and organizing them in Excel, is another time-consuming venture. The timeliness of that information is very paramount, as well. Classifications for specific items might need to be rotated quarterly, if not monthly. By automating ABC inventory reporting, you can have sales and inventory numbers auto-extracted and updated within your spreadsheets for easier reporting. You can also set up these updates to happen in real-time, fueling better adaptability to rotating classifications per different seasons. All in all, the benefits you get across individual inventory reports are similar—you boost efficiency, work much faster, and gain time back to work on more business-critical projects. When considering the *amount* of different reports needed on a regular basis, however, the benefits stack up in a profound way, and leave you with a much more scalable way to manage your inventory. ## How to automate inventory reporting **‍**Effective inventory reporting is key to streamlining your operations and making informed decisions. With Parabola, you can automate and optimize your inventory reporting system for greater accuracy and reduced manual labor. Here’s how: #### 1. Streamline inventory management reporting **‍**Parabola makes it simple to [consolidate data from multiple source](https://parabola.io/use-cases/consolidated-inventory-reporting)s, allowing you to create real-time dashboards for inventory management reporting. Track key metrics effortlessly and reduce manual errors in your workflows. #### 2. Leverage inventory reporting software for automation Parabola's workflow automation capabilities eliminate tedious manual updates and allows you to focus on strategic decisions. #### 3. Optimize planning and inventory management Parabola supports seamless integration with inventory planning tools and existing systems, helping you tackle both inventory planning and management. Stay ahead of demand with real-time updates and actionable insights. #### 4. Scale with inventory planning software and solutions Whether you use standalone inventory planning software or integrated inventory planning solutions, Parabola helps you scale efficiently. Automate repetitive tasks to ensure your inventory management planning processes evolve with your business needs. With Parabola, automating your inventory reporting system and optimizing inventory planning and management has never been easier. Whether you’re working with advanced inventory planning tools or integrating with ERP systems, Parabola empowers you to simplify workflows, save time, and make smarter decisions. --- # (Webinar) Introducing Visualizations in Parabola Source: https://parabola.io/blog/visualizations-webinar We built Parabola to help non-technical teams take control of their most manual processes — they’re dominated by disparate data that lives in spreadsheets, unstructured emails and PDFs, and a slew of other data sources. When you pull together these data sources in Parabola, you’re not only automating your workflows, but you’re also documenting your processes as you build them. [Data visualizations in Parabola](https://parabola.io/updates) let you further document and centralize your data through custom reports that show your work. And unlike a traditional BI tool, you can easily create one-off reports without the help of a data or engineering team. Because your reporting lives in the same platform as your logic, non-technical teams can easily make changes to reporting dashboards and visualizations. In this webinar, you’ll learn: - How to create custom dashboards using your data that can be updated as frequently as you want, whether that’s every week or every hour. - Different ways to customize your visualizations: Whether you want to view your data tabularly or you want a tiled view sliced and charted in various ways, you can create it in Parabola. - How to share data visualizations and their underlying logic with your team to provide context on a given process. --- # How to automate repetitive workflows with Visual Basic in Excel Source: https://parabola.io/blog/vba-automation Excel is one of the most frequently used digital platforms because of its wide-ranging capabilities, but it can take a bit of massaging and training to truly unlock its full potential and automate menial, repetitive tasks. Visual Basic in Excel opens up a whole new world of automation. Below, we’ll look at what VBA is and whether you should use it to automate Excel if you’re in retail or e-commerce. ### **What is VBA?** [Visual Basic for Applications (VBA)](https://www.investopedia.com/terms/v/visual-basic-for-applications-vba.asp) is a Microsoft programming language that can be used to extend and improve the functionality of Microsoft products, including Excel, Word, PowerPoint, and Access. In essence, it allows you to custom code specific add-ins, functions, and actions into your Excel worksheets. The biggest [benefit of VBA](http://www.nobledesktop.com/learn/vba/why-learn-vba) within Microsoft Excel is that it allows you to define and set your own automations. By using VBA, you can automate repetitive tasks and the creation of unique forms, data reports, and dashboards. ### **VBA vs. Macros** The conversation here is less about VBA *versus* macros, but instead how they work together — since that ultimately is the relationship between the two. Visual Basic is the programming language that sits behind all Microsoft applications. So, a macro is actually created, defined, and edited *using* VBA. That makes VBA the key ingredient behind all of your [Excel automations](https://parabola.io/blog/how-to-automate-excel). The possibilities of what you can do are close to unlimited, as long as you have the developer know-how to do so. Let’s look at some common use cases as examples. ### **Common VBA functions** While there are plenty of very unique VBA automations you might identify over time, there are a number of common go-to automations that [retail and e-commerce teams](https://parabola.io/solutions/operations) can use in Excel. #### Excel VBA array function The array function allows you to define any number of different elements that share the same intrinsic data type as related, or as one group/variable. Arrays are most useful to manipulate data ranges and summarize large bulks of data more quickly. For example, let’s say dairy products are one component of your inventory, but you have many different types of dairy products. Using an array, you can establish “dairy” as the variable, whereas individual product names would fit in as elements within that variable — “organic 2% reduced fat milk,” for example, would sit underneath “dairy” as a sub-category. So if you have, say, 28 different dairy products, instead of creating 28 different variables, you can create one variable comprised of 28 elements. When building or automating formulas, you’ll be able to do so on a variable-level, without having to account for 28 separate elements. It makes data manipulation much easier and much cleaner. #### Excel VBA remove duplicates function The “remove duplicates” function within Excel is pretty self-explanatory. It helps you formulaically identify and remove duplicates from any specified range. Using VBA, however, this process can be automated. Perhaps you do regular inventory data uploads, and within that, certain parts of product names are going to be duplicated — for example, the brand name, color, or other specific descriptor of a product. By creating [automated duplicate removals](https://www.automateexcel.com/vba/removing-duplicate-values/) via VBA, whenever you upload new inventory data, you won’t have to run a manual prompt to clean and consolidate your data. #### Excel VBA VLookup function The VLookup function allows you to search and identify a value from a specific column within a defined range, and return a corresponding value from a different column. Using VBA, you can build VLookup functions into your worksheets to automate specific workflow steps. A real-world example of using VBA VLookup in Excel would be to use it to fill in pricing data across invoicing and reporting — from individual orders, to full [sales reports](https://parabola.io/use-cases), profitability reports, and forecasting reports. By building VLookups into your worksheets, you could automatically pull pricing data from an existing worksheets into new worksheets and reports. #### Excel VBA else if function In VBA, the "ElseIf" statement is used within conditional statements (like an "If" statement) to specify further conditions that should be tested when the original "If" condition is false. Suppose you wanted to categorize purchases based on the geography of your customers. Your initial “If” statement might say something like, “If location = New York Then,” in which case you’d follow that statement with a function or formula you want to be carried out. The “ElseIf” statement would then follow *that*, working as a backdrop in the case that the original If statement is false (in this case, if the associated location on an order is not New York). The ElseIf statement would essentially say, “if the location is *not* New York, then check if it’s [location B],” to which you’d then again define a function or formula you’d want to be carried out in the case that the second statement is true. ### **How to use VBA to automate Excel workflows** To automate Excel workflows using VBA, you need to write specific macros that perform the individual tasks or sequences of tasks needed to carry out your workflows. In general, that requires you to utilize the VBA editor within the “Developer” tab in Excel, to insert a module, where you’d then input the code required to build and run specific macros. Let’s look at some specific examples of how retail and e-commerce brands can use Visual Basic in Excel to [get rid of inefficient Excel tasks](https://parabola.io/blog/juice-press-saved-200k-year-by-building-their-workflows-in-parabola). #### Examples of VBA automation ##### **1. Creating custom functions with VBA and automating simple data manipulations** Of course, it’s important to first understand the basics of VBA automation. In the video below, you can see an example of how to create custom functions. **More specifically, the video shows two things:** 1. How to auto-apply discounts to individual customers based on their order quantity. 1. How to auto-clear sales data periodically (based on your billing cycle). These are both examples of how powerful VBA can be for invoice creation and automation. If you’re on a weekly billing cycle, you can use VBA to clear old/unneeded transaction data each week. When generating new invoices, based on quantity (and perhaps geography or other factors), you can automatically manipulate price totals based on the quantity and product type of each order. ##### **2. Automating reporting processes with VBA** Another major timesaver is to automate reporting with VBA. In this next video, you can see how to auto-generate a report based on a specific variable present in your data — that could be geography, product type, product name, or even individual items. In this case, you’ll see how to do so by product name. This is great for something like inventory on hand reporting. If you have 40-50 different product names, you’d have to filter and run through each to update your current on hand numbers. With VBA, you can automate this process, so that, by simply running a macros, you’ll auto-generate as many on hand reports as you need, defined by as many variables as you need. ##### ‍**3. Automating email sending with VBA** Something else that is incredibly helpful for invoice automation and sending, is using VBA to automate email outreach to your customers. You can see how to do that in the following video. In the video, you can see how to use VBA to build a macro to build and personalize emails, add attachments, and auto-send emails without having to leave Excel. So, after automating the creation of your invoice, you can also automate the sending of those invoices to your customers, or the *re*-sending of those invoices near or after their intended due date.**‍** ##### **4. Creating a dashboard and automating transaction monitoring with VBA** VBA automations can also give you real-time visibility into your inventory and warehouse activity. The video below shows how you can automate transaction monitoring and the creation of dashboards. More specifically, the video shows how to automate the use of transaction forms to collect data that can then be used to automatically generate transaction dashboards and monitor activity. This is also a tremendous help for inventory on hand reporting, as you can build live dashboards that get updated as transactions happen, instead of requiring more periodic (and more manual) audits. ### **Is VBA the right tool for your automation?** In the end, VBA is amazing for automating repetitive spreadsheet-based tasks and reporting. The two major caveats, however, would be: ‍**1. It requires coding knowledge. If your team does not have coding experience, VBA is likely going to be too challenging.** ‍**2. It’s not collaborative. While you can run macros when a file is shared, the code that’s been built to power the macro cannot be shared, nor can it be edited after the file is shared.** If either of these caveats are a detractor for you, you’d want to look for an alternative for your VBA automation. ### **Alternatives to VBA automation in Excel** Parabola works as a great alternative to VBA for retail and e-commerce companies looking to automate their Excel workflows. Parabola allows you to set invoicing, reporting, and many other custom automations across your supply chain, no code required. [Setting up automations and workflows](https://parabola.io/product-overview/building-a-flow) is made simple through customizable drag-and-drop modules. As your business changes or grows, Parabola allows for easy and collaborative real-time workflow changes, with centralized access across teams. Your team can also build custom [data visualizations](https://parabola.io/updates) and establish a single source of truth for your workflows. Whatever the case, automating your Excel workflows is one of, if not the most immediate way to increase supply chain efficiency and set up your e-commerce engine to scale seamlessly. Schedule a demo --- # Flexport drives over $8M a year in ROI by automating their essential workflows in Parabola Source: https://parabola.io/blog/flexport-case-study Flexport is on a mission to make global trade easy for everyone. With over 10,000 brands that rely on their end-to-end supply chain services, Flexport moves tens of billions of dollars worth of inventory across the globe each year. Like many supply chain businesses, Flexport was fueling much of its day-to-day operations with manual workflows centered around spreadsheets and email. Not only is this inefficient and costly, but it's hard to fully trust your data when so much of it is managed manually by different people. Flexport came to Parabola in search of a solution, and they identified and realized significant savings in multiple areas of their business. ### The challenge: Achieving a scalable cost to serve while streamlining error-prone manual workflows A number of teams at Flexport were stuck using manual and labor-intensive processes. To streamline their supply chain process in an industry that hasn’t changed in decades, the Flexport team needed support in three critical areas: - **Data reconciliation** to standardize and build trust in carrier and vendor data - **Document digitization** to streamline complex Customs filing processes - **Operational visibility** to better understand the status of any given shipment Flexport’s Customs operations and bill processing teams use Parabola to streamline their supply chain operations in each of these three areas. ### The solution: Reconciling data, streamlining document ingestion, and implementing more granular operational visibility Flexport deals with massive amounts of carrier data on a daily basis that comes in different formats, from different sources, and with different endpoints. They needed a quicker, more accurate way to reconcile their data from sources like Customs filings and internal reports to automatically feed it to the right places. So, what does this look like? #### Automatically recouping fees for customers via duty drawback Flexport has dedicated staff that manages duty drawback for their clients: When they can connect the importation of goods to exports or destructions, they are able to help their customers recoup 99% of their import duties, taxes, and fees. This is a main revenue driver for Flexport. In order to file drawback claims, Flexport uses Parabola to reconcile data from various sources such as: - Internal databases - Shipping data from suppliers - Government portals like NetCHB and ACE By automating drawback filing in Parabola, Flexport delayed hiring additional headcount by four to six months, resulting in **$50k in labor cost savings.** They also increased drawback filing throughput by 50%, resulting in an additional **$1.6M in savings per associate — 10% of which is captured as revenue.** Over the last year, Flexport has helped their customers **recoup millions of dollars**through drawback as a result of automating their drawback preparation process. > “Drawback is extremely complex, but it's an essential part of our business. Parabola has allowed us to seamlessly pull in complex shipment data from multiple sources to consistently deliver on our promise of recouping the majority of applicable Customs fees,” said Tim Vorderstrasse, Director of Drawback at Flexport and President of the Atlanta International Forwarders and Brokers Association. #### Streamlining freight audit and invoice validation while maintaining visibility The freight audit team at Flexport lived in Google Sheets and Excel. They were downloading hundreds of spreadsheets each day with invoice data to validate shipments and charges. To further complicate matters, many of their customers had unique shipping rates and rate calculations. This meant Flexport had to manually create unique spreadsheets for each client with individualized calculation requirements to account for these differences. > "Before Parabola, we were spending countless hours in spreadsheets every day for our freight audit process, and with all the manual work, we still couldn't fully trust our outputs. Since automating the process, we’ve been able to recoup significant duties, taxes, and fees. We've also seen huge labor savings and we know our audits are accurate," said TJ Mitchell, Freight Audit Manager at Flexport. Flexport uses Parabola to automate the bill auditing process that pulls in data from various sources such as Looker and Google Sheets. The final output is an Expected Cost (EPC) report, which helps them categorize vendor bills by low, medium, and high-risk for defects. Automating the bill auditing process has enabled the team to focus more of their energy on medium to high-risk vendor bills. This means they can scale across new regions internationally and new modes like trucking and air — and do so without hiring additional headcount. The Parabola Flows they’re using have saved hours per week for the team, **amounting to savings of hundreds of thousands of dollars in labor each year** — not to mention the peace of mind Flexport has knowing that the amounts they’re paying to their vendors are accurate. #### Digitizing essential documents, seamlessly Flexport files and processes thousands of critical documents for their customers on a daily basis. All of this information is sent via email as a PDF or Excel file, which meant Flexport was stuck manually ingesting and filing thousands of forms daily. Flexport now uses Parabola to ingest, clean, and format documents for seamless filing. Now, they are anticipating thousands of dollars per year in cost savings after implementing their document digitization Flows in Parabola. > Gilles Lagast, Global Director of Business Operations, Customs at Flexport, said “The value Parabola has added to internal reporting and preparing workflows at Flexport is huge. We've seen such a dramatic reduction in our cost to serve since implementing, and Parabola has been a key partner in our quest to reduce cost to serve and performance transparency.” #### Always knowing shipment status to avoid demurrage To avoid accessorial fees, Flexport must move thousands of containers within the time allowed before their last free day (LFD). There is a lot of data from carriers that is automated and also presented in the form of flat data files to parse through. > “We've got thousands of containers at any given point that we have to keep track of, and we have to know when each of them is going to start incurring fees. This was a substantial challenge before but Parabola has made it a reality,” said Mark Cummings, Director of Supply Chain Operations at Flexport. Flexport now uses Parabola to standardize and consolidate shipment data and validate key milestone details. They built a Parabola Flow that generates a real-time report of select data points for open shipments and even sends automatic alerts to the appropriate carriers when there are data discrepancies. By automating the process of the data validation reporting, Flexport has the opportunity to substantially reduce their time to serve, with potentially **tens of thousands of dollars of time savings annually.** And by consistently alerting carriers and internal teams on data flags, they stand to protect themselves and their customers from **hundreds of thousands in demurrage fees over the coming months.** ## The results For Flexport, Parabola is a tool to create smarter workflow processes, resulting in a considerable reduction in fees, time, and overall cost to serve. Since they have removed so much of their manual, error-prone work with Parabola, the team at Flexport can now trust the outputs they’re generating and spend more time on strategic work. > “Parabola is a game-changer delivering tangible results and revolutionizing our data operations. By harnessing its solutions, we have automated a broad range of complex data tasks, leading to **cost savings and expedited revenues that now amount to millions of dollars each year.** The seamless automation has not only freed up invaluable time and resources, but it has also unlocked previously untapped revenue channels, further fueling our growth," said Lagast. Most importantly, Flexport has leveled up their workforce. Before, they were completing data-intensive and mission-critical workflows manually in spreadsheets and email. The processes were not only error-prone due to being completed by hand, but they took hours per week to complete. Now, Flexport has empowered employees of all backgrounds — technical and non-technical operators alike — to create their own solutions for the problems they know best. They now have the ability to build whatever Flow they need to make their work more accurate and efficient, without the use of any software engineering resources whatsoever. By diligently reconciling data, streamlining the digitization of essential documents, and optimizing their alerting and reporting processes, Flexport is seeing an **ROI of more than $8.27M per year across cost savings and revenue acceleration** as a result of using Parabola for their essential workflows.To learn how to automate your team's mission-critical workflows, set up a time to chat with our team. Schedule a demo --- # How to Get the Most Out of Your 3PL Management Process Source: https://parabola.io/blog/3pl-management If you’re a retailer, it’s more important than ever to make sure you’re working with the right [3PL](https://parabola.io/blog/3pl-management) warehouse management systems and partners. With a market valuation of [over $1 trillion in 2022](https://www.grandviewresearch.com/industry-analysis/third-party-logistics-market), it’s clear that the demand for 3PL is nothing short of massive. Due to e-commerce consumer demand, there are more warehouse and storage needs than there have ever been. [Retailers](https://parabola.io/solutions/operations) are managing higher volumes of inventory than ever and shipping in less time, all while consumer expectations continue to rise every day with each delivery. There are a lot of things that can get in the way of meeting these expectations. For example, rising demand can make it hard for retailers who sell perishable inventory to forecast accurately, leading to increased costs from spoilage and inventory write-offs. Large parcel retailers deal with more damage to [in-transit inventory](https://parabola.io/blog/track-in-transit-inventory), a cost they need to factor in, but that they would also like to avoid. A key way to mitigate fluctuations in your supply chain costs, is to work closely with 3PL partners that are best for your inventory needs. Choosing the right 3PL systems and companies can make all the difference in terms of cost savings and customer satisfaction. Here, we’ll look at what a 3PL is, how to know if it’s right for you, and how to manage and get the most out of your [3PL partners](https://parabola.io/blog/3pl-management). ## What is a 3PL? A third-party logistics provider, typically referred to as a 3PL, is a company that retailers outsource to for warehouse management and supply chain management. [3PL services](https://parabola.io/blog/3pl-management) typically include warehousing and storage, inventory management, picking and packing, shipping and receiving, transportation, and software management. Working with a 3PL partner can [benefit you](https://www.chrobinson.com/en-us/resources/blog/what-is-a-3pl-and-how-can-they-benefit-your-business) in many ways — it can help reduce costs, improve storage and shipping efficiency, and help you scale your business more seamlessly. For retailers with specialized needs, it’s essential to choose the right [3PL partner](https://parabola.io/blog/3pl-management), because many 3PLs are logistical experts, and they can help you build the customized processes and systems you need to have a competitive advantage over other brands in your industry. ## Is a 3PL right for you? There are a couple of considerations when determining whether or not third-party logistics are right for your business. #### You’ll need to determine your specific warehouse management needs If you have more specialized inventory or very disparate needs across your inventory, then you’ll have much more complicated logistical needs. Those needs can be hard to meet at a reasonable cost and ease of management. Take perishable inventory as an example: Perishable inventory requires extra urgency given the shorter shelf life of products. Storage needs to be close to manufacturers and packers to ensure inventory is moved quickly enough. Products also need to be produced and delivered in due time to avoid spoilage. Without third-party help, it can be very difficult, and very *expensive*, to set up your perishable inventory logistically, not to mention the lack of flexibility as you scale. Given their existing vendor relationships, locations, and expertise, 3PLs can tap into their network more flexibly to help you set up your ideal logistic infrastructure. You could, for example, [find a 3PL](https://parabola.io/blog/3pl-management) with access to multi-temperature storage and shipping, and built-in safety and inspection clearances you might take more time to get (for example, USDA clearance). There are 3PLs built like this for virtually any specialized inventory storage or handling need. Take large parcel inventory as another example. Large parcel shipping tends to be costly. Given the size and weight of large parcels, they’re handled by more people [in transit](https://parabola.io/blog/track-in-transit-inventory), and they’re more likely to get damaged in transit, which can have significant cost implications. These cost implications extend beyond just the cost of production and lost sales. You’re also looking at time spent in freight claims, the cost of return shipments, as well as customer dissatisfaction due to delays or receiving damaged goods — so the shipping mode and route can make or break a sale, given [consumers’ growing expectations](http://www.axlehire.com/blog/exceeding-consumer-and-shipper-delivery-expectations-today/) for delivery. To avoid hefty cost implications and lowered customer experiences, you can work with a 3PL that specializes in large parcel warehouse management and shipping, perishable inventory, or any other specific type of inventory. #### Determine whether you have the bandwidth to manage your own inventory Managing your own inventory can take quite a bit of work, especially as your inventory becomes more specialized, as previously mentioned. Warehouse management, storage, and shipping all require you to be hyper-aware of the present while forecasting and building analyses for the future of your business. Across all types of inventory, there are seemingly endless tasks to manage: you need to procure goods, establish manufacturer and vendor relationships, track inventory in real time, replenish and rotate inventory, pick, pack, ship, receive, handle returns, and manage software, data, and analytics. Workflow tools like Parabola can make inventory management easier and can be used for better [inventory visibility](https://parabola.io/), [demand forecasting](https://parabola.io/), [invoice parsing](https://parabola.io/blog/parsing-pdfs-with-parabola), KPI reporting, and more. Without a deep understanding and expertise across all of these areas of fulfillment, trying to manage everything yourself can become a time and money drain that only gets worse as you scale. 3PLs can serve a major benefit to your business by offloading the specialized responsibilities of inventory management. #### Realize you will need to relinquish some level of control As it goes with any third-party relationship, working with a 3PL means your warehouse and inventory may not be managed exactly how you would manage them on your own. 3PLs all operate under different procedural guidelines. They have their own culture and communication styles which may shift how your team needs to operate internally. Ultimately, trusting the expertise of 3PLs is in your best interest, but you need to be aligned with their brand, order of operations, and even their security protocols: you need to trust that your 3PL partner is going to keep sensitive business or customer data safe. Be sure to reach out to 3PLs to ask explicit questions about their practices, communication styles, security, and so on. ### 3PL examples Say you’ve found the perfect [warehouse management system](https://parabola.io/the-supply-chain-tech-stack-report-lp) and know that you’d like to work with a 3PL, but you just haven’t found the right partner yet. Some examples of 3PLs that cover most modern omnichannel ecommerce needs include: - [DCL](https://dclcorp.com/) - [Shipbob](https://www.shipbob.com/) - [Stord](https://www.stord.com/) - [Deliverr](https://deliverr.com/) - [Shipfusion](https://www.shipfusion.com/) - [ShipMonk](https://www.shipmonk.com/) - [Flexe](https://www.flexe.com/) ## How to get the most out of your 3PL partnership Once you’ve decided on a 3PL partner, or if you already have one, there’s still a lot that you need to stay on top of to make sure the relationship is benefitting you the way it can and should. This is largely about keeping track of inventory, communicating, and tracking the performance of your 3PL partner or partners. #### Keep track of inventory Since you’re not directly managing inventory, it’s important that you stay aware and informed. To do this, you should always have an inventory management dashboard that gives you an idea of inventory levels and accuracy. With Parabola, you can consolidate across multiple warehouses and 3PLs to build reporting dashboards that provide more visibility into your inventory and warehouse logistics. You can pull in data from virtually any source — PDFs, Shopify, spreadsheets, emails, and so on — to build workflows and data visualizations for [inventory on hand](https://parabola.io/use-cases/inventory-reconciliation), demand forecasting, backorder reporting and alerting, and more. Of course, you can always [track inventory manually](https://smallbusiness.chron.com/keep-track-inventory-manually-21920.html), but this is much a more frustrating, time-consuming, and imprecise process. #### Communicate often Plain and simply, whenever you’re working with a third party, you should never assume that they know what is happening internally at your business, nor that you know everything that is happening within their business. Don’t assume that they know what you need, even if they are the logistical experts. You might have specific on-hand inventory requirements, replenishment expectations, or shipping and delivery preferences. Always communicate your needs to your 3PL and be sure to keep your 3PL partner updated with news on your end as well. Give them some insight into your demand forecasting and reporting. Let them know when you anticipate fluctuations or disruptions in order volume. Keep them up to date with any technology upgrades or potential new warehouse management [integrations](https://parabola.io/product/integrations) coming down the line. At least in the beginning of any 3PL partnership, it’s always better to err on the side of overcommunication. #### Track their performance Keeping close tabs on 3PL performance is perhaps the most important factor for you to actually impact efficiency and operate at your greatest potential, and this is especially true if you’re using multiple 3PLs. If you’re not using 3PL data meaningfully to optimize processes, you’re going to have a hard time making improvements and scaling operations. To track the performance of your 3PLs, you should set clear KPIs, set up and automate [scorecarding](https://parabola.io/use-cases/carrier-scorecard-reporting), and even visit your warehouses from time to time. ##### Set KPIs As you would for your own internal teams, you should define specific [KPIs for each of your 3PLs](https://www.techtarget.com/searcherp/tip/3PL-KPIs-that-can-help-you-evaluate-success). By setting KPIs around inventory accuracy, shipping and order accuracy, manufacturer lead time, and fulfillment costs, you can set benchmarks for success, and specifically track performance back to overall company growth goals. ##### Build and automate 3PL scorecarding with Parabola Based on key logistics KPIs and other performance indicators, you can build out and automate [3PL scorecards](https://www.google.com/search?q=parabola+scorecard&oq=parabola+scorecard&gs_lcrp=EgZjaHJvbWUyCQgAEEUYORigATIGCAEQRRg8MgYIAhBFGDwyBggDEEUYPNIBCDI3OTdqMGo3qAIAsAIA&sourceid=chrome&ie=UTF-8) within Parabola. Across different warehouses and geographies, you can track back to your KPIs and grade 3PL partners based on defined benchmarks for inventory performance, shipping and order accuracy, and cost efficiency. You’ll be able to bake in and answer questions such as: - What is the rate of stockouts, delays, or canceled orders? - How many replacement orders have there been? - How many expensive, last minute shipments have been made? - What is the rate of product spoilage? - What reviews have we been receiving from consumers/customers? Answers to these questions allow you to see if and where performance is lacking, while also giving you something tangible to show your 3PL when discussing ways to improve. ##### Physically visit your warehouses If only from time to time, it’s also helpful to physically visit your warehouse locations to track performance and get a better feel for *why* things might be going positively or negatively. A quick in-person audit might be a bit more time consuming, but could still give great insight into inventory accuracy, order accuracy, shipping performance, and so on. Overall, with the right combination of 3PLs, warehouse management systems, and workflow tools, you’ll be able to grow your business more effectively, and spend more time on strategic initiatives instead of attending to errors, cleaning data, and trying to repair relationships with vendors and consumers that should not have been fractured to begin with. ## Key takeaways: How Parabola simplifies 3PL management Managing third-party logistics (3PL) operations efficiently requires automation and seamless data flow. Here’s how Parabola helps optimize 3PL management workflows: #### 1. Automate inventory and order management Keep stock levels accurate and orders flowing smoothly by syncing data across your 3pl's warehouses, order management systems, and shipping providers. #### 2. Improve warehouse and freight operations Streamline 3pl warehouse management, shipment tracking, and freight coordination by automating data transfers and reducing manual updates. #### 3. Enhance visibility and reporting Centralize logistics data to get real-time insights into inventory, fulfillment, and shipping performance without the need for spreadsheets or manual tracking. #### 4. Scale 3PL management operations efficiently As your logistics network grows, Parabola’s automation ensures that your systems remain connected, accurate, and easy to manage. Parabola makes 3PL management more efficient by automating data flow across 3PL logistics, inventory management, and freight operations. Whether you need to optimize a 3PL provider workflow or integrate a 3PL warehouse management system, Parabola helps you streamline operations and scale with confidence. --- # Excel Automation: 9 Essential Methods to Save Time, Reduce Errors, and Streamline Your Workflows [Updated for 2026] Source: https://parabola.io/blog/how-to-automate-excel Around [1 billion people](https://earthweb.com/excel-users/) worldwide still use [Microsoft Excel](https://www.microsoft.com/en-us/microsoft-365/excel). Why? The capabilities within the platform are virtually endless. For whatever need your team has, there’s a function or formula that does exactly what you’re looking for. As the volume of datasets (and the speed at which we manipulate them) continues to increase, Excel automation is becoming increasingly useful for long-term efficiency and workflow scalability. *Here, we’ll look at the benefits of Excel automation, which functions you should be automating, and how to do so.* ## The benefits of Excel automation Excel automation involves using tools or scripts to “set and forget” specific tasks, which creates a lot of benefits for you and your team. ### Time savings Excel automation significantly reduces the time spent on repetitive, manual tasks — whether you’re [validating invoice data](https://parabola.io/blog/freight-audit-process) or [generating payroll reports](https://parabola.io/blog/juice-press-saved-200k-year-by-building-their-workflows-in-parabola). You can save an abundance of time on planning, modeling and forecasting, and attribution by automating tasks like these. By having scheduled, automated triggers in your workflows, you can avoid bottlenecks almost entirely. ### Cost savings Automating manual tasks improves productivity and lets your team focus on the strategic roles they were hired for, which means you’ll reduce labor and software costs. With the same amount of people, you’ll be able to get more work done in less time. By automating repetitive tasks, you won’t need to pay for contractors or third-party tools as often. ### Improved accuracy Manual data entry and manipulation are prone to human error. Automating Excel functions eliminates the need for manual human intervention, which increases data accuracy and ensures consistency over time. For example, [freight invoices contain errors about 20% of the time](https://www.joc.com/article/tms-founder-pivots-self-service-freight-invoice-auditing_20201027.html) (which can add up to huge costs for shippers). By automating the validation of these invoices, shippers can dramatically improve the accuracy of their payments. Automation in Excel can help you better manage your datasets from the start and eliminate duplicates and mismatches. ### Scalability Given the time and cost reduction that comes with automating Excel workflows, as well as the uptick in accuracy, your processes naturally become more scalable. As tasks become more complex or the volume of data increases (or both), your team only has to put the work in upfront to set up new automations. Automated workflows can adapt to changing business needs and requirements relatively easily, since it makes it simpler to incorporate new processes or adjust existing ones. ## How to automate in Excel Excel automations are one of [various ways](https://www.youtube.com/watch?v=dmOuROMfPb0&t=77s) to build internal automated workflows. So, when teams automate in Excel, what do they generally do? The first step is setting up automations to record, review, or update information across any number of spreadsheets. That might mean logging data from invoices, purchase orders, or other documents (digital or physical). Next is setting up automations to update or review existing data and datasets. After everything is logged, automations can be set to continuously validate everything being input. The last step is automating notifications and triggers so there’s no delay in between steps, and so the user (or the next automated step) knows to launch. Automated triggers serve as the foundation of productivity behind any workflow. ### How are Excel automations done? #### Macros Defined [by Microsoft](https://support.microsoft.com/en-us/office/run-a-macro-5e855fd2-02d1-45f5-90a3-50e645fe3155) as “an action or a set of actions that you can use to automate tasks,” macros allow you to use programming language to record a set of steps so you can run that process on repeat whenever you’d like. Depending on the task you’re automating, macros can be a bit complex to set up, but they’re a great way to automate spreadsheet-based tasks. #### Data validation rules Specific to data *entry*, setting validation rules allows you to automate the cleaning of data and bring structure and cleanliness to anything you’re doing within Excel. #### Pivot tables Pivot tables are often used to summarize or surface insights from larger datasets. Pivoting data is one of the main ways teams automate reporting within Excel, taking the manual manipulation out of surfacing specific insights. #### Plug-ins and third-party tools There are a bevy of third-party tools you can use to automate within Excel — including a long list of [plug-ins](https://learn.microsoft.com/en-us/office/dev/add-ins/excel/excel-add-ins-overview) that integrate directly to automate specific functions. In most cases, you can search the web for the exact action you want to automate and you’ll find a number of plug-ins or software tools that’ll make the work easier for you. #### Contractors and freelancers Many teams still rely on contractors to set up their automations. While a less scalable approach, it is sometimes a quicker way to set up automations in Excel. ## Nine Excel automation methodologies you should adopt Here are the Excel functions you should look to automate first given the time and cost savings you stand to gain. ### 1. Find and Replace The Find and Replace function in Excel allows you to search for specific text or numbers within an entire worksheet or selected range and replace those values with different text or numbers. By using this function, you stand to save a ton of time making manual changes and ensuring accuracy by renaming cells or correcting errors in bulk. #### How to automate Find and Replace You can use Microsoft’s Power Automate feature to set specific Find and Replace automations via custom scripts. You can also use Parabola to automate [Find and Replace](https://parabola.io/transform/find-and-replace) tasks. ### 2. Remove Duplicates The Remove Duplicates function helps you identify and remove any duplicate values within a selected range in your worksheet. Assuming duplicates aren’t intentional, removing them helps improve accuracy and consistency, and it’s an important function for increasing the trustworthiness of your data. #### How to automate removing duplicates Typically, you’ll have to use data validations within your worksheet to automatically remove duplicates, or turn to Power Automate to set custom macros. See how to automate removing duplicates by pulling your data into Parabola [here](https://parabola.io/transform/remove-duplicate-rows). ### 3. Transpose The Transpose function is a tool that allows you to re-format data from a row structure into a column structure, or vice versa. It is the act of rotating an existing table. Transposing or rotating a table helps you rearrange data in a way that better suits your visual or practical reporting needs. This lets you set up your data to be more easily turned into reports, graphs, or any other needed output. #### How to automate Transpose Automating the Transpose function directly in Excel will require a bit of code via macros, but you can do it completely without code by using [flip tables](https://parabola.io/transform/flip-table) in Parabola. ### 4. Extract Text Extracting text from a worksheet generally refers to isolating specific portions of text from a larger dataset. This can be done a few different ways. LEFT, RIGHT, and MID Excel functions allow you to extract values from the left, right, or middle of a text string (i.e. extract a prefix from a column of information) — for example, you may want to extract just the month from a date, or just the city from a location defined as “City, State.” You can also simply search text, or use the Find and Replace feature to hack your way to text extraction by removing the part of the value you don’t need. #### How to automate text extraction Automating text extraction would require a macro or set of formulas to first find and identify the text you’re looking to call out, then replicate that text in another location in your worksheet. You can use Parabola to easily [extract text](https://parabola.io/transform/extract-text-from-column) from a dataset to create new columns of data based on the extracted values. You can also use one of Parabola’s AI steps, [Extract with AI](https://parabola.io/transform/extract-with-ai), to extract whatever pieces of information you need from any data source, including PDFs. ### 5. Text to Columns The Text to Columns function in Excel allows you to take values that are combined within a single column and separate them into multiple columns — in other words, you can split them based on specified parameters. This is most useful for segmenting and structuring your data to help improve accuracy and analysis by way of better organization. #### How to automate “Text to Columns” Again, to do this directly in Excel, you’ll need to use macros or VBA to set custom scripts to automate this function. In Parabola, you can easily pull in data and [split columns](https://parabola.io/transform/split-column) based on your custom set delimiters. ### 6. Consolidate The Consolidate function in Excel allows you to consolidate data from different sources, whether that’s across columns, rows, or worksheets. This function is powerful if you’re looking to summarize strings of data from any number of sources. You can centralize your information and insights for cleaner, better analysis. #### How to automate consolidating data Other than using the Consolidate function, Power Query, or a VLOOKUP, you’ll have to utilize custom scripts here as well in order to set up further automations to consolidate information from multiple sources. #### 7. Categorize Data with AI Categorizing data is the process of taking free-form or inconsistent text and assigning each row a clear, structured label. This helps you organize messy datasets, group similar values together, and create cleaner reports without manually reviewing every line. Using AI to categorize your Excel data lets you instantly convert scattered entries into consistent categories — whether you’re grouping SKUs, tagging transactions, labeling suppliers, or organizing survey responses. It removes the manual “read, decide, type” loop and gives you clean, analysis-ready data. ##### How to automate data categorization Automating categorization directly in Excel usually requires lengthy nested formulas or custom VBA. With Parabola, you can do it completely without code by using the [Categorize with AI](https://google.com/search?q=categorize+with+ai+parabola&oq=categorize+with+ai+parabola&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIICAEQABgWGB4yDQgCEAAYhgMYgAQYigUyDQgDEAAYhgMYgAQYigUyDQgEEAAYhgMYgAQYigUyDQgFEAAYhgMYgAQYigUyCggGEAAYgAQYogQyBwgHEAAY7wUyCggIEAAYgAQYogQyCggJEAAYgAQYogTSAQgzMzM1ajBqNKgCALACAA&sourceid=chrome&ie=UTF-8) step to scan each row, assign a category, and output a clean, structured column automatically. ### 8. Standardize Values with AI Standardizing data means transforming inconsistent or mismatched values into a single, uniform format. This includes correcting typos, normalizing variations (“NY”, “New York”, “N.Y.”), cleaning date or number formats, and making sure all entries follow the same rules. AI-powered standardization helps ensure your dataset is clean, trustworthy, and ready for downstream operations like reporting, joining tables, or exporting to other systems. It eliminates the hours typically spent hunting down inconsistencies and manually cleaning them up. #### How to automate data standardization Standardizing values in Excel often requires complex find-replace sequences or carefully written formulas. In Parabola, you can automate the entire process using the [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai) step, which analyzes your data, normalizes similar values, and outputs a clean, unified column with no coding required. ### 9. Calculate Days Between Dates Calculating the number of days between two dates is a common task across operations, logistics, finance, and reporting. Whether you’re measuring lead times, cycle times, aging, durations, or delays, manually running date-difference formulas can become slow and error-prone. Automating this calculation ensures every row gets an accurate, consistent “days between” value — even as new data is added. It’s a foundational building block for SLA tracking, forecasting, and performance measurement. #### How to automate calculating days between dates Excel has several date-difference formulas, but maintaining them across large spreadsheets can be tedious. In Parabola, you can automate the entire process by using the [Compare Dates](https://parabola.io/product/transform/compare-dates) step, selecting your two date columns, choosing the output unit (days/months/years), and generating a new calculated column instantly. ## Using Parabola's Excel automation tools Recording Macros in Excel is one way to automate repeatable spreadsheet-based tasks, but you’re limited in what you can do with the data and the learning curve can be steep. This is where Parabola comes in. Anything you can automate by recording a Macro in Excel, you can do by dragging and dropping steps into a Parabola Flow. Think of Parabola steps as Excel functions — for any function you can perform in Excel, there’s a Parabola step that will allow you to complete the same task. [If you're ready to see how it could work for your team, give it a try today.](https://parabola.io/app/signup) ________________________________________________ ## FAQs ### 1. What is the best way to automate tasks in Excel? The best way to automate Excel tasks is to combine built-in functions (like formulas, PivotTables, and Power Query) with no-code workflow tools that can run tasks automatically. This reduces manual work, eliminates repetitive steps, and ensures data refreshes without opening the file. ### 2. Can Excel automate data cleaning? Yes. Excel can automate parts of data cleaning using formulas, Flash Fill, and Power Query. For more complex cleanup — like standardizing inconsistent values or categorizing free-form text — AI-powered tools can automate the entire process without custom logic. ### 3. How can I automate repetitive tasks in Excel without using macros? You can automate tasks without macros by using Power Query, formulas, and no-code workflow platforms that run transformations for you. These approaches avoid VBA entirely while still letting you automate imports, cleanup steps, calculations, and reporting. ### 4. What are the most common Excel tasks that should be automated? Common tasks worth automating include data imports, date calculations, text cleanup, categorization, standardization, deduplication, formatting, and report generation. Automating these steps saves time and reduces human error. ### 5. Is AI useful for automating Excel workflows? Yes. AI is especially useful for tasks that require interpretation — such as categorizing messy text, standardizing variations, suggesting corrections, or identifying trends. It removes manual review and helps produce consistent, reliable outputs at scale. ### 6. How do I know which Excel tasks are worth automating? Any task that is repetitive, error-prone, takes more than a few minutes, or needs to be repeated daily/weekly is a good candidate for automation. If you’ve ever copy-pasted the same steps twice, it can likely be automated. ### 7. Can Excel automatically update reports? Yes. With Power Query, formulas, or an external no-code workflow, Excel reports can refresh automatically when new data arrives. This helps teams keep dashboards and KPIs up-to-date without manual updates. ### 8. What tools can help automate Excel outside of Microsoft Office? Tools like Parabola, Zapier, Make, Python, and Google Workspace extensions can automate Excel workflows by importing data, transforming it, and exporting updated files — all without needing VBA or macros. ### 9. Can I use AI to extract Excel data? AI tools can pull structured information out of raw or messy Excel files without manual copying or formatting.[With Parabola, you can automate this extraction process end-to-end](https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-excel-data) — upload or sync the file, let AI interpret and structure the data for you, and automatically pass the cleaned output into the rest of your workflow. --- # What Is Your Freight Invoice Audit Process Missing? Source: https://parabola.io/blog/freight-audit-process ## What is a freight audit? Simply put, [freight audit and payment](https://www.traxtech.com/blog/the-ultimate-guide-to-freight-audit#:~:text=Freight%20audit%20and%20payment%20is,into%20a%20company's%20transportation%20costs.) is when a shipper reviews a freight invoice from a carrier to ensure they aren’t overcharged. In this process, the shipper conducts a freight bill audit for trucking and air shipping costs, demurrage, and more, comparing the actual cost to the estimate on a rate card provided by the carrier so they can recoup any charges they don’t owe. ## What makes the freight audit process so challenging? Because freight audit requires managing thousands of freight invoices that come in all different formats, the process of standardizing and preparing invoices for a shipping audit is extremely tedious and time-consuming. The shipping data lives in emails, spreadsheets, and PDFs with non-standard columns, sections, and headers — and getting it all into one place is a headache, to say the least. Each complicating factor in the [freight audit process](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets) is worth solving for one reason alone: the opportunity for cost savings. #### Error-prone freight invoicing processes Why is it that such a high number of freight bills have errors that cost importers of record millions of dollars each year? Many companies that use legacy transportation management systems have been around for decades — some freight companies haven’t updated their systems [since the dawn of transportation management systems in the early 90s](https://www.e2open.com/blog/how-transportation-management-systems-tms-simplify-global-logistics/). Fragmented data sources like spreadsheets, email, and PDFs, and lots of manual work all contribute to a less-than-streamlined process — and before now, there have been few [solutions](https://parabola.io/solutions/operations) to combat this. And because all of the touchpoints involved (PDF freight invoices, rate cards in emails and spreadsheets, TMSes, and more) aren’t built to work together, data often gets lost in translation. This happens in countless ways — for example, the same line item may be defined or [categorized](https://parabola.io/product/transform/categorize-with-ai) differently by different carriers, even though they’re the same thing (i.e. “trucking wait fee” vs. “per diem”). Even for modern freight companies, optimizing the flow of data so that it can be trusted, requires work and buy-in from the entire organization. A fundamental lack of trust in data means that companies are often unable to accurately forecast landed costs. If they don’t pay carriers and other partners within their negotiated terms, they risk facing big penalties. To avoid facing penalties, shippers are forced to spend hundreds of thousands of dollars a year allocating valuable employee time to validating freight bills and rate cards manually, and they’re still not able to get trustworthy outputs. Outsourcing this work isn’t a viable option either, because it costs just as much and provides even less visibility into essential data. #### Freight contracts that are too complex The way shipment costs are calculated isn’t cut and dried. There are highly variable expenses like fuel surcharges, accessorial charges for services beyond pickup and delivery, surcharges for any value-added services like palletization, sort and segregation, or dedicated trucks for changing delivery locations, and more. Some carriers have rate cards with multiple calculation methods for their various freight lanes. For example, ocean shipments moving on the same lane may have either a fixed rate or a spot rate, and rates can vary. With thousands of shipments and transport invoices to validate during freight audit, these calculations get hard to keep track of very quickly. For businesses with particularly complex supply chains (like companies selling perishable goods), the shipping audit process has to reflect these complexities: they need to use warehouses and shipping partners who have access to the right storage facilities to keep things fresh. Not only do these retailers need to diligently [track and trace](https://parabola.io/blog/track-in-transit-inventory) every step of the shipping process, but they also incur added costs for refrigeration as well as special packaging and insulation. When you’re doing this process manually for hundreds or even thousands of shipments, regardless of what you’re shipping, it’s incredibly complex — not to mention error-prone and time-consuming. In a freight audit, the shipper needs to figure out exactly what each charge was for, and understand every surcharge and shipment cost calculation method. If they’re lucky, the carrier will make the process easier by providing information about each charg — but that’s not always the case. #### Absent or incomplete shipment data Data loss is inevitable when validating and combining thousands of shipments through multiple systems. Without complete and comprehensive shipment data, the shipping audit process can be painfully long and manual. During a freight audit, you may spend days trying to find the root cause for late deliveries or service issues, because like with added costs, the carrier likely isn’t going to volunteer that information to you. Some shippers who are stuck with their legacy systems are forced to make educated assumptions about the status of a freight invoice or what’s included on a rate card. Not only does this add a lot of work up front to verify information, but it adds more time and cost on the other end: either the claims process will be held up because your information is incorrect, or worse, you’ll never even find out about overages that you’ve paid because they got lost in the shuffle. ## How the freight audit process can be improved Manually managing the inner workings of a freight audit and payment process is a huge undertaking — and doing it with 100% accuracy is nearly impossible. By removing the manual aspect of the workflow through automation, you stand to see significant time and labor cost savings, and your data becomes way more trustworthy. ### Saving time and costs No manager wants to hire a team of skilled people only to have them responsible for managing [freight audit](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets) workflows that shouldn’t have to be done manually — and that should [run on their own](https://parabola.io/blog/parsing-pdfs-with-parabola). The amount of employees’ time that must be spent verifying invoices adds up to massive opportunity cost. Not only that — it also means that no one has the time [to do the jobs they were actually hired for](https://parabola.io/blog/parabola-is-for-operators), because they’re too busy keeping [freight audit processes](https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets) afloat. By automating your freight bill audit process, you can eliminate manual processes like uploading data into your TMS, comparing actual shipping costs against a rate card, and submitting claims for overages. You can allocate less money toward labor, and by removing manual work, you’ll also catch more invoicing errors and recoup more of the costs you don’t owe. ### Improving data integration Automating your freight audit drives precision. When you [automate freight audit](https://youtu.be/VDlxGfwlXU4), you’re ensuring that each point in the process works infallibly with the next. For example, with Parabola, you can [integrate any system and data source](https://parabola.io/product/integrations) you’re already using — whether it’s an emailed PDF, Google Sheet, Looker, or a TMS — so they work together seamlessly to generate accurate shipping cost reports. It’s also incredibly easy to update the workflows you build, so if your rate card or another part of your process changes, it takes a few minutes to adapt across your entire workflow. Trusting your shipping data not only makes freight audit go faster from invoice preparation to the claims process, it also allows for better decision-making. With automatic data discrepancy resolution, analysts can trust that the data they’re looking at is correct and up to date. And they can actually focus on their expertise — optimizing logistics and allocating resources more effectively — to improve profitability. The freight audit and payment process is essential, but it shouldn’t be a headache. To learn about how to get back the time your team is spending on the freight audit process and how you can better trust your data, set up some time to chat with our team. ## Freight invoice automation with Parabola Parabola transforms your freight and parcel audit processes by automatically ingesting data from carrier CSVs, PDFs, and third-party portals, then compares that data against rate cards to find overages or inaccurate charges. With most carriers providing only 30-60 days to submit a dispute, audit velocity is key for any team shipping at scale — and auditing in spreadsheets leads to human error, unscalable process, missed savings, and limited visibility into billing discrepancies across carriers. Beyond reconciling values like parcel rates and fuel surcharges, Parabola can also audit more complex values like accessorial fees, weight and dimensional charges, service level failures, and volume-based discounts. Even if your rate cards and invoice PDFs are totally unstructured and lack standardization. ‍ --- # Announcing Parabola's $24m Series B Source: https://parabola.io/blog/announcing-parabolas-24m-series-b I’m incredibly thrilled to announce we’ve raised a $24 million Series B led by [OpenView](https://openviewpartners.com/), [Matrix](https://matrix.vc/), [Thrive Capital](https://thrivecap.com/), and other existing investors participated, and we’re welcoming some fantastic new partners including [Flexport](https://www.flexport.com/), who is also one of our favorite customers. More on the round and what’s next in a minute, but first a note on where we’ve been. At Parabola, we have a core belief that everyone, particularly the in-the-weeds folks closest to problems, are uniquely capable of being problem solvers and solution builders. For me, this belief arose out of a cognitive dissonance I’ve had all my life about how empowered I’ve always felt working with and programming computers, compared to how disempowered most of my friends and most of my family have felt working with technology. In the years before Parabola, I consulted for some of the world’s largest CPG, retail, and logistics brands. Across them all, I met remarkably smart, curious, and creative operators stuck doing things manually. They were drowning in spreadsheets, PDFs, and emails so much that they were unable to implement what they knew were the real solutions to doing things a better way. Parabola was born to help these people leverage technology to solve their problems, their teams’ problems, and ultimately prove that having a STEM background is not required to build things that have massive economic and societal impact. This vision has taken time to become reality. The mission-critical operators who are our best users are so busy that they need truly world-class tools to be confident in taking the time to change their processes. And companies need to realize the benefits of shifting from siloed operators manually running fractured pieces of a business to holistic teams collaborating together and sharing with their stakeholders. This is a bold new way of operating. Over the past few years, we’ve been working quietly but relentlessly with forward-looking companies like Sonos, [Flexport](https://parabola.io/product/integration/flexport), Uber Freight, Durex, and Bain to help them realize this dream of operational excellence. We’ve helped their operations teams stop throwing headcount at problems, implement solutions for things that change too regularly for their technical teams to write custom code, and leverage the latest AI models to [standardize](https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data) the inconsistent data they deal with daily. We’ve seen individuals inflect their careers and companies inflect their growth. Now in 2023, every company (and every operator) needs to pursue operational excellence if they want to participate in the future. And we’re finally ready to bring the power of Parabola to all. So back to the fundraise. This new investment reinforces the notion that 2023 is the year of the operator. Raising funds in this environment is tough, especially a Series B, but it’s the companies that bring true, tangible ROI that will succeed — and we believe Parabola can and will do so. We can’t wait to show you the new features we’re working on. We’re so incredibly grateful to Parabola’s entire support system, and I wanted to say a huge thank you: - To our amazing customers who regularly see the best version of product and company we can be and who remind us daily why this work matters - To our investors, who have been extremely supportive as we’ve taken our time to build a strong, credible, aligned-with-our-customers foundation and truly believe in our ability to empower non-technical users to leverage automation and AI - And most importantly, to our team, for their intense customer focus and their commitment to evangelizing our mission in their daily work. Here’s to the next phase of Parabola’s journey!‍ ‍*We’re excited to officially welcome the following new investors: Immad Akhund (CEO and Founder of Mercury), Jack Altman (CEO and Founder of Lattice), Cristina Cordova (COO at Linear and early Stripe and Notion exec), Twum Djin (Engineering Leader at Stripe), Toby Espinosa (VP at DoorDash), Flexport Ventures, AJ Frank (Senior Director of Product at Meta), Matt Hertz (Co-Founder of Second Marathon), Zack Kanter (Co-Founder and CEO of Stedi), Vlad Magdallin (Co-Founder and CEO of Webflow), Praveer Melwani (CFO of Figma), Otherwise Fund, Kyle Parrish (VP of Sales at Figma), Jeff Raider and Good Friends (Founders of Harry’s, Allbirds, and Warby Parker), Sarah Scharf (VP of Marketing at Vanta), J Zac Stein (Former CPO and President at Lattice), Melissa Tan (Early growth exec at Dropbox and Webflow), and Jen Vaughan (COO at Assembled).* --- # Juice Press Saved $200k+/year by Building Their Workflows in Parabola Source: https://parabola.io/blog/juice-press-saved-200k-year-by-building-their-workflows-in-parabola [Retail and ecommerce teams](https://parabola.io/solutions/operations) use Parabola to access and activate their data and automatically streamline their dynamic workflows. Parabola allows teams to go beyond the spreadsheet with its drag-and-drop interface. Teams can automate their most time-consuming workflows and create a single source of truth for how a process works, allowing for more efficient and accessible collaboration for the whole team. Here’s how [Juice Press](https://juicepress.com/), an organic grab-and-go health food provider, uses Parabola: #### Challenge Juice Press had tons of manual workflows, like order management, tip allocation, and accounting, eating up hundreds of hours per week. #### Solution They built Parabola Flows to automate their most essential (but tedious and time-consuming) processes. #### Results Juice Press saved hundreds of hours per week and hundreds of thousands of dollars per year. ## The challenge Before Parabola, Juice Press was manually completing processes across multiple parts of their business, including finance, HR, payroll, and their production team, overall spending hundreds of hours per week on manual, repetitive tasks. Two processes in particular were eating up most of this time: 1. First was their ordering process for each of their 85 store locations. Ariana Korman, Juice Press’s COO, had to manually update inventory and ordering information for each of their 85 store locations. 1. Second was their tip allocation process for all in-store employees. Each location was separately managing their own tipping pools in a manual process that required HQ to pull four or five reports into their accounting software, and without tip distribution software, this could be challenging. Even worse than taking up time, these manual tasks widened Juice Press’s margin for error and hurt their operating margins. Juice Press had a few goals: First, they wanted to get their employees’ time back. They didn’t want to have to continue hiring people to keep up these manual processes. Second, they wanted to improve how these processes were run and get rid of inefficient Excel tasks. ## The solution Juice Press was looking for a solution that was customizable, but also powerful enough to automate all of the tasks they needed to optimize. When they started out with Parabola, they set up a few Flows to address some of their most essential workflow inefficiencies. The first of their Flows were meant to automate inefficient Excel processes, like tip allocation. They created a 38-step Flow that pulls in tip data for a specific date range from the point of sale from each store location through a [direct integration with Square](https://parabola.io/integration/square); it can easily replace any tip distribution software. It pulls data from their payroll system to determine hours worked by each employee within the same date range and generates a report in a format compatible with their payroll system, allowing them to allocate tips accordingly. They also set up two different Flows to automate ordering inventory for each store location: The first Flow was built to create order templates. It pulls every store’s desired (“par”) and actual inventory levels from MarketMan, their restaurant management system, to automatically calculate order quantities and create order templates for each location. The second Flow ingests the data from each of these order templates into Parabola to create orders through MarketMan's API. These Flows let Juice Press forecast the par level based on sales and compare it with real-time inventory to automatically order the correct amount. As Juice Press saw the efficiency that Parabola created, they began to view it as an essential workflow optimization tool with tons of opportunity for time and labor cost savings. They took on the position that anything that was time-consuming, manual, and repetitive should be done in Parabola. > “I would be very upset if I could no longer use Parabola,” said Ariana. “I would probably hire another person, because our team just doesn’t have the capacity right now to go back to doing things manually. I would end up having to hire another full-time salaried employee to do what Parabola does.” Having set up their own Flows initially, Juice Press ultimately opted for a Parabola plan that allowed for more one-on-one support in building out API connections and more complex workflows. This way, Juice Press could be in control and be participants in their own success — and it’s working out well: > “Whenever something comes up, if I email anyone from the Parabola team, they get back to me immediately. The response time is always awesome, and they're always extremely helpful. I don't feel like I'm left to manage a system alone.” ## The results At this point, Parabola is saving Juice Press over 400 hours per week in manual work and over $200,000 a year across the whole company. With the tip distribution Flow, HR and Payroll teams are saving a total of 200+ hours per week in manual work, which translates to over $2,000 per week in labor costs for the company. By automating their ordering workflow for each store location, they’re saving 15 hours per week and eliminating the need to hire full-time help just to keep up their manual workflows. Juice Press is meeting their goal of empowering each of their employees to play into their own skill sets and do the jobs they were hired to do in the first place. “With Parabola,” Ariana says, “we can focus energy and brainpower on things that require thought, strategy, interpretation, and judgment as opposed to things that are manual — just pressing buttons.” By implementing Parabola, the Juice Press team has unlocked a ton of possibilities for increasing the efficiency of workflows across their business. They’re actively working with Parabola to identify new Flows to continue to uncover new and creative ways to save time and money. To see where Parabola can help you save time and resources, set up a demo with our team: Get a demo --- # Ecommerce aggregators accelerate when data moves freely Source: https://parabola.io/blog/ecommerce-aggregators-accelerate-when-data-moves-freely Operationalizing large datasets while selling on marketplaces like Amazon, Walmart, and eBay takes a lot of time and resources. Brand Managers have access to tons of data and valuable insights into the competitive landscape, and they need to be able to operationalize and act on that data in a structured and repeatable way (especially when it's trapped in a data warehouse). Read on to learn how Parabola makes it easy to standardize process and reporting, making data accessible to operators and enabling teams to understand their growth levers. ### Maximizing the value of your data warehouse The valuable data your team needs to make decisions likely exists in a data warehouse or siloed sales channels, however it’s often extremely difficult to turn millions of records into actionable reports in real time. Especially when there are never enough tech resources to build that custom Snowflake report or new internal tool. Parabola allows you to turn data trapped in your data warehouse into real-time custom reports without waiting on an engineering backlog. ### Search term analysis With tools like Datahawk, Junglescout, and others, you have access to a lot of valuable keyword data, but it can be difficult to get actionable insights from huge amounts of data across siloed sources. In Parabola, you can extract and aggregate this data to make it easy to access and understand. You can also notify your team about keyword performance in real time. This way, you can adjust your spend and make decisions to increase conversion in a timely way.[‍](https://parabola.io/blog/30-actionable-ai-use-cases-across-supply-chain-finance-and-operations) [Learn how you can use AI in Parabola to identify high-performing keywords and generate new keyword suggestions based on the highest performing ones.](https://parabola.io/blog/30-actionable-ai-use-cases-across-supply-chain-finance-and-operations)‍ If you’re only interested in monitoring a certain number of keywords for each product, you can filter and trim data before sending reports to your team. By automating reports in Parabola, you can spend more time managing the things that matter and less time on the things that don’t. Here’s an example of a Parabola Flow that could pull inventory data from multiple sources, filter by search volume, and send a report via email and Slack: ### Preparing for acquisition When you acquire a new brand, you acquire a whole new dataset and all of the problems and nuances that come with it. Standardizing this data, understanding parent-child product relationships, inventory reconciliation, and brand velocity become top of mind. In Parabola, you can share, copy, and replicate pre-built logic that’s already used for brands in your portfolio, creating workflow consistency across brands. This makes it more efficient for your team to onboard new brands and reduce data debt. And because account-specific operating silos are torn down and processes are standardized, you can do it without increasing headcount. To see how Parabola can help you streamline data processes across brands, set up a time for us to show you how it works: Schedule a demo --- # How to Parse PDFs With Parabola Source: https://parabola.io/blog/parsing-pdfs-with-parabola In Parabola, you can pull data from pretty much anywhere — [Google Sheets](https://parabola.io/product/integration/google-drive), [APIs](https://parabola.io/product/integration/api), FTP folders, databases, and more. But sometimes the data you’re handling are less *structured*, like third-party invoices or digital documents. Now you can parse data directly from [PDFs](https://www.adobe.com/acrobat/about-adobe-pdf.html) (and other unstructured data sources) and make that data usable in your existing workflows. Extract whatever information you want from a PDF, whether it’s line-item data that exists in tables, or it’s document-level data, (like date or invoice number). Our customers use these steps for a bunch of different use cases, like: [Doing this in Parabola](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet) is easy and intuitive — plus it's powered by AI. ## How to parse PDFs with Parabola [‍Parsing PDFs can seem daunting](https://parabola.io/blog/best-methods-pdf-parsing), but with tools like Parabola, the process becomes straightforward and efficient. Here’s a quick summary of the main points to help you get started: #### 1. Start by importing your PDF file **‍**Use [Parabola's intuitive interface to upload and prepare your document](https://parabola.io/product/integration/pdf-file) for parsing. Whether you're converting a PDF to an Excel spreadsheet or tackling complex tables, the first step is always setting up your input. #### 2. Define your parsing rules **‍**Tailor the process to [extract](https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-pdf-data) the data you need. Parabola lets you break down the details and effectively parse a PDF into manageable pieces, making it easy to structure your data. #### 3. Convert PDFs to Excel or spreadsheet formats **‍**Once your data is parsed, it can seamlessly be transformed into other formats. #### 4. Automate and repeat the process **‍**Save time by setting up reusable workflows. Parabola allows you to automate the conversion and parsing process, so you can quickly and easily convert PDFs whenever requird. #### 5. Analyze and export your data **‍**After parsing and organizing your data, export it to your preferred tools. With Parabola, learning how to parse a PDF file becomes a streamlined experience, helping you move from scattered documents to structured insights in no time. Learn more about parsing data from PDFs here: [Ready to try it out? Dive in and see how easy it is to parse PDFs to Excel or any other format you need.](https://parabola.io/app/signup) --- # Working With PDFs Is Finally Automatable Source: https://parabola.io/blog/working-with-pdfs-is-finally-automatable Parabola already makes it easy to work with tabular data: rows can be filtered, columns can be added, and entire tables can be combined and transformed in sophisticated (and automated) ways. Tables are a natural format for an organization’s data to live in. But what happens when the data shows up in something *other* than a table? Until now, the answer has typically been that *someone has to do the manual work* to process that data for your organization. Whether it’s a vendor invoice, a delivery list, or even a monthly bill, you’re often left with a teammate having to handle numbers *by hand* to keep your internal workflows humming. At Parabola, we can’t stand that kind of thing! So, per popular request, we’ve released a feature that lets you [parse PDFs and automate Flows from them](https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet). It’s the first PDF automation capability of its kind, and it will open up new automation opportunities in your organization. ### PDF-powered automations PDF files seem to go hand-in-hand with manual processes. So in making PDF intake compatible with Parabola, we didn’t stop at simply letting you upload them: you can actually *automate*with data from PDFs in your Flows. That’s why our first PDF-enabled step is [Pull from Email Attachment](https://parabola.io/integration/email-attachment): you can auto-parse documents — CSV/TSV, XLS, and now PDFs — sent to this step and use their data in Flows. You can even [trigger a Flow from this step](https://parabola.io/product-overview/updating-and-running-your-flow), so that the Flow will automatically run when a new PDF is sent. With that, you’ll have best-in-class PDF automation integrated into your workflows, all without writing code! ### Processing PDFs in the real world We’ve already seen exciting use cases for PDF automation. Here are just a few: - Collecting warehouse reports and extracting inventory data - Standardizing data — like due dates and terms — from vendor invoices and purchase orders - Intaking bills and preparing them for accounting - Standardizing PDF data tables to send to a transportation management system (TMS) - Intaking customer-provided PDFs for regulatory, customs, or reporting purposes - Digitizing bills of lading (BOLs) and packing lists for freight shipments - Categorizing and cleaning invoice line items using AI ### Try it yourself Our PDF processing step is ready to try for customers on our [Advanced plan](https://parabola.io/pricing). For existing customers: connect with us and we’ll set you up according to your needs. For those new to Parabola, sign up for a demo and tell us you’d like to use PDFs in your Flows. Get a demo ‍ --- # AI Has Arrived for Operators Source: https://parabola.io/blog/ai-has-arrived-for-operators AI is changing the playing field for businesses worldwide. The rise of large language models like OpenAI’s GPT is awe-inspiring, a bit otherworldly, and we believe it’s the beginning of enabling Operators to do their best work, faster. To date, Parabola has thrived in the hands of Operators that work with business data every day. Parabola Flows empower non-technical teams to make quick action with structured data from multiple sources — like spreadsheets and databases — driving efficiency and reducing redundancy — without the need for engineering. But we know that everyday data is often messy, unstructured, or variable. Imagine if you could automatically use the interpretive power of AI to decipher ambiguous and unstructured data — like PDFs, text messages, emails — then [standardize](https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data), enrich, and categorize it for you… Today, we’re introducing [AI steps](https://parabola.io/product/security/using-ai-steps) to Parabola, powered by GPT. The power of AI is unleashed within the friendly guardrails of Parabola’s drag-and-drop interface. Beneath the hood, each step is powered by a series of finely tuned requests to GPT that optimize for accurate and repeatable results. It’s exceptionally accessible and extraordinarily capable. The new [AI steps](https://parabola.io/product/transform/experiment-with-ai) will help you extract [standardized](https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data) data from messy inputs, [categorize data](https://parabola.io/product/transform/categorize-with-ai) into consistent buckets, and experiment with generating new columns based on existing entries. ### Categorize with AI [Categorize with AI](https://parabola.io/transform/categorize-with-ai) enables you to leverage GPT to evaluate incoming data and then assign categories to each row. Parabola will create a new column and assign your categories there. For example: - Shopify stores can process a list of product names and [categorize](https://parabola.io/product/transform/categorize-with-ai) them by department — like clothing, home goods, electronics, grocery. - Marketers can categorize the email addresses in a user database by type — like personal, school, work, and government emails. ### Extract with AI ‍[Extract with AI](https://parabola.io/transform/extract-with-ai) uses GPT to evaluate the data that you input and extract the specific pieces of information that you ask for. All you have to do is name the new columns in Parabola and the AI will extract the relevant data in a consistently formatted way. This is a massive improvement for anyone working with PDFs, messy excel files, text from the body of emails, and other unstructured data sources. It’s pretty incredible what’s possible with just a single step. For example: - Operations teams can process inconsistently formatted invoices and extract the amount, due date, sender information, and even line items from each into a consistently formatted table. - Marketing teams can pull in a list of customer survey responses and extract key fields they’d like to compare — like product ordered, competitors evaluated, and whether or not they’d recommend. ### Experiment with AI [Experiment with AI](https://parabola.io/transform/experiment-with-ai) lets you write in plain text how you’d like to change your existing data or even generate entirely new columns. It’s the most flexible (and fun!) [AI step](https://parabola.io/product/transform/experiment-with-ai). For example: - Ecommerce teams can enrich product information management systems by [pulling data](https://parabola.io/docs/parabola-university/101/fundamentals/5-pulling-data) about each product and generating unique product descriptions based on the data. - Ecommerce teams can also ask AI to identify Amazon keywords with the highest rank increases within a given time period and auto-generate suggestions for similar keywords. - Sales teams can pull a list of company names and generate email domains for each. ### Put AI to work Our mission at Parabola is to democratize the ability to access and take action on data by empowering non-technical teams with technical tools. Now with the power of highly accessible and immensely capable AI, we can’t wait to see what you build. Try now To learn more about how AI in Parabola can help you put your data to work, schedule some time with our team: Get a demo --- # Leverage AI in Your Flows With Gpt Source: https://parabola.io/blog/leverage-ai-in-your-flows-with-gpt At Parabola, we’re focused on helping Operators achieve their highest potential. What makes this possible is cutting down on the manual work required of them each day to keep everything operating as it should.Operators can achieve much more than that, which is why we’re excited to be seeing them use creative solutions in their Flows, like OpenAI's [GPT](https://parabola.io/integration/gpt-3).GPT presents a ton of opportunities to leverage the power of AI in your Flows. Operators can use GPT in Parabola to create reliable solutions without having to know anything about code or machine learning. Here are a few of the use cases we are exploring: ### Normalize and standardize data Long-form user-generated data like emails, texts, and documents sent in from vendors contain useful information that is easily readable by humans — not so much by machines.For that reason, with Parabola alone it’s hard to reliably parse content and extract the right information…enter AI. Large language models like GPT do well at parsing content and summarizing the details that you want to know. Combine this with the power of Parabola, and you can easily normalize and [standardize](https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data) your data in a reliable way. ### Categorize data Leveraging GPT’s ability to extract specific and detailed information builds upon Parabola’s strength in structuring data. With the integration, you can categorize data into practically any category.For example, you can group email attachments into categories like “Invoices,” “Importer Security Filings,” “Bills of Lading,” etc. Using Parabola with GPT lets you get specific information in a consistent, reliable structure. ### Enrich existing data Using GPT in Parabola even allows you to add on to the data you have with data you need. GPT’s vast access to knowledge allows you to further enrich your data in ways you wouldn’t be able to without AI.For example, if you have a data set that contains customer first and last names and email addresses, you can pull other information attributable to each customer, like company name, job title, and LinkedIn URL. ### Perform sentiment analysis Enabling the power of AI in Parabola lets you leverage new techniques, like sentiment analysis. GPT in Parabola makes it easier to get a sense of the overall sentiments of your customers.You can take a list of Amazon reviews, for example, and [categorize](https://parabola.io/product/transform/categorize-with-ai) them by sentiment: positive, negative, or neutral. This is a much faster way to aggregate and understand customer opinions so you can more easily make informed decisions.We’re just getting started with exploring all of the potential for large language models in Parabola. Currently, we’re working on a native integration that will offer more robust ways to leverage some of these use cases, including attributing dedicated steps in the Flow.Can’t wait for the native integration? [Fill out this form](http://bit.ly/3Lkc1OX) to share a unique use case for Parabola and we’ll put you on a waitlist for early access.To learn more about how you can currently use GPT in your flows, check out our How-to article: Learn more --- # How Flexport Uses Parabola to Save Millions Each Year Source: https://parabola.io/blog/how-flexport-uses-parabola-to-save-millions-each-year ### About Flexport Flexport moves tens of billions of dollars in goods across the globe, and leads innovation in the supply chain. This monumental task depends on razor-sharp execution. A freight forwarder is a kind of shipping coordinator. When a container moves from point A to B, complicated handoffs need to happen at each step – like telling the sender and receiver where the container is, coordinating the trucks and ships and trains, and handling all the documents that need to be filed with the government. Flexport believes that **automation** is key to powering the global supply chain. They’ve put billions of dollars to work proving this thesis. They hired an army of great engineers to build software that brings the entire shipping process online – so for the first time, buyers and sellers can get real-time shipment updates. Parabola is deeply philosophically aligned with this thesis. Automating manual workflows and promoting process transparency and standardization is core to what we do! So when it became clear that we could help Flexport automate something they couldn’t solve on their own, we were excited to roll up our sleeves. ### The problem Parabola now supports countless teams and use cases across Flexport. We’ll illustrate its application by focusing on an initial challenge they looked to us for help with. If you’re trying to bring a container into the United States, your freight forwarder must file an “ISF” form with Customs 48 hours before it gets on the water. Customs demands to know what’s in the container, where it’s going, who’s shipping it, and so on. Flexport dutifully files that form for thousands of containers every day. But every shipping carrier around the world gives Flexport that information in a different format. The carrier works with scores of freight forwarders and ships to a hundreds of countries, so they aren’t able to get the information into a format that US Customs will accept. That means Flexport has to take on that burden. This was a perfect test of Flexport’s thesis that everything involved in supply chain should be automated – the challenge is making it happen. The input formats were complicated, and each carrier submitted things in a different way. With data formats and processes often changing, any code would need constant maintenance, and the solution would never catch up to the problem. Without numerous engineers dedicated to them, Flexport’s Operations Team was forced to handle the process manually. So contrary to the company’s DNA, their operators were spending nearly half of their time searching for, keying in, and reconciling data across emails, spreadsheets, and other systems. Moreover, leadership understood that the number of redundant, time consuming, error-prone, and morale-draining processes would only grow as the company took on more customers and launched more services. ### The solution One of Flexport’s competitive differentiators is their ability to allow customers to work with them on their own terms. As such, the first thing we needed to prove was that Parabola could support the hundreds of ways that data would be sent to them from their customers and partners. Specifically, they needed us to automatically grab data from emailed CSVs, APIs, and online form submissions. From there, we needed to demonstrate our ability to clean up and normalize the disparate data – something that was previously done manually in spreadsheets. Since Parabola can handle any work that someone would be doing in a spreadsheet (e.g. reformatting, vlookups, pivots), we were well positioned to automate the entire intake, transformation, and entry of this data into their proprietary platform. As a result: in **less time** than it previously took an operator to manually process a single ISF form (roughly one hour), someone equipped with nothing more than an understanding of the problem (and certainly without any coding experience) was able to build out a Parabola Flow that automated the processing of **hundreds** of forms per day, without any manual intervention. Since automating their ISF workflow, Flexport’s Operations Team has looked for opportunities to drive further efficiencies. When they encounter specific transformations or pieces of logic that are broadly applicable, the team codifies them in [Card Templates](https://parabola.io/product-overview/cards#card-templates). Templates allow the team to seamlessly share Flow building blocks with their teammates, shortening feedback cycles and spreading best practices. In a matter of weeks, teams using Parabola at Flexport have reduced their cost to serve their customers by as much as 50%, increased each employee’s capacity by ~40%, and eliminated the need for additional headcount and engineering support. By eliminating their manual, redundant, and error-prone tasks, Flexport’s teams are freed to focus on the work that requires subject matter expertise and human intervention. They can invest in process improvements and forward-looking innovation. The success of Flexport’s Operations Team’s success has inspired other departments to follow suit. Complex workflows across customs, demurrage, drayage, pricing, and invoicing have all moved away from spreadsheets and onto Parabola. To date, the total value of their cost savings and revenue acceleration is approaching 8 figures (yes, that means north of 10M), and we’re only getting started. --- # Powering PLG through Rev Ops Automation Source: https://parabola.io/blog/powering-plg-through-rev-ops-automation This post is for sales and rev ops leaders who are being asked to hit a crazy revenue target (shoutout to all the VCs out there) but do so with fewer resources during a recession. I can’t imagine my job without the tools we’ve developed over the last few months so I wanted to share in the hope of adding value (and perhaps even building some pipeline). Earlier this year, I joined Parabola - a fast-growing startup that helps companies with complex operations, that are spread thin and don't always have the headcount, software or eng resources needed to scale. And while we have the same (cliche) artisanal cold brew and ping pong set up that we had during my days at Salesforce and Segment, that’s probably where the similarities end. Nowhere were these differences more apparent than when it came to our infrastructure and resourcing (or lack thereof) in the realm of sales ops. If you ask anyone in sales ops, their MO has always been “doing more with less” so perhaps more than any other function, they are built to last in today’s climate. Fortunately, “more with less” is really core to what we do - so it should come as no surprise that with without any sales ops experience, we’ve been able to develop something that isn’t *that* far off from what’s considered best of breed. What’s more impressive is that it was all done without writing a single line of code and was built by one of our AEs (shoutout to Adam). Any sales org needs a pulse on the following: - Leading indicators of potential upsell/churn risk - Customers who approached key milestones only to drop off - Integrations and features leveraged across segments Accessing this information sounds simple but I dare any non-engineer to try to harmonize stripe, CRM, marketing, support & product data AND keep it going in real time. We need answers quickly – and we don’t have time to wait on data and eng to build or buy long-term solutions. And they’d certainly prefer to not drop what they are doing each day, week or month whenever I ask for data. The burning question for rev ops, sales, and marketing leaders then becomes: **How can we put tools in place that help the team be successful without burdening them with repetitive work or relying on technical teams?** ## Rev Ops at Parabola To answer that question, we can draw on our own PLG experience. It’s first important to understand what PLG looks like at Parabola: 1. User discovers Parabola and creates an account 1. While creating their first flow, they pull data from a core system (like Looker, Salesforce, Google Sheets, or Snowflake) to build an automated workflow 1. This flow is published and triggered to run automatically in the future 1. Based the value of this automation (which didn’t require engineering support), this user invites a colleague to Parabola and the cycle (hopefully) repeats Now that we understand the high-level motion, let’s now look at some of the data-points contributing to this growth: - Number of users on a team, and team growth over time - Number of triggered automations running - New integrations configured - New reports being created and shared Knowing that, you can imagine how excited our team gets when a team invites more users to Parabola, or connects to their database for the first time. These moments represent prime opportunities for our Team to ensure customers find maximum value by taking advantage of all relevant features in an effort to deliver real value (and also power the growth flywheel). ## Example Use Cases We have no less than 20 workflows at Parabola powering rev ops. None of these automations required any engineering resources, and have helped us leverage our CRM as a real source of truth, proactively touch base with key customers, and act on opportunities quickly. Some of those automations include Flows that… #### Capitalize on product activity - Flag user activity in Slack or email, triggered by in-product activity such as adding new users to a team, deploying new automated workflows, and connecting to a new data source for the first time - Enroll a customer in an email sequence based on specific functionality leveraged (or not leveraged) in the platform #### Handle inbound leads - As new leads are created in Salesforce throughout the day, a flow assigns those leads to the “next up” account executive and surfaces leads with a score above a certain threshold in Slack #### Enrich CRM records - A ‘reverse ETL’ workflow pulls product data from our Redshift database, matches those metrics with Salesforce records, and enriches our CRM with product analytics to paint a more complete picture of leads, contacts, and accounts - Based on the source of a given lead, their record may not be enriched as it’s created in Salesforce; another workflow finds these un-enriched records, pulls data from various tools to enrich that record, and pushes the complete record back to Salesforce #### Calculate sales commission - Pull data on closed deals to give reps real-time visibility into variable commission, accounting for complexities such as clawback and accelerators #### Report on customer health - Use product and CRM data to assign health scores to key customers, enabling our CX team to proactively reach out to customers at risk of churning Bundled together, these automations help us drive revenue and positive customer experiences by taking action at the right time. It also cuts out the need for regular Salesforce/ database CSV exports, where we formerly needed to manipulate datasets in a spreadsheet to answer questions through data. By having our Sales and CX teams build these automations together, we’ve also identified repeatable best practices for calculating user metrics that contribute to health scores and reporting. When we look at reports, there are never any questions around data integrity because we know we’re all calculating results the exact same way, every time. ## Takeaways No two rev ops teams are the same. We evaluated third-party solutions for a handful of processes before embracing the fact that we needed a tool that was as custom and flexible as our processes themselves. For the rev ops customers we work with, commission structures differ, data stacks vary, intent indicators are custom to specific products, and CRMs require different types of enrichment. This limits the effectiveness of traditional enablement tools with specific point-to-point integrations and pre-built workflows. Parabola was built to enable operators to seamlessly aggregate data across sources and surface actionable insights to the right people at the right time. Being able to execute on these ideas in hours or days as opposed to weeks or month (and keeping our engineers focused on creating an amazing product) is part of what makes me so excited about bringing Parabola to the world. ‍ --- # Why Parabola Is for Operators Source: https://parabola.io/blog/parabola-is-for-operators ## **Who are Operators** When businesses confront tough challenges, they turn to Operators. Operators are fueled by ingenuity and determination to make things happen. They have different titles — maybe Analysts, Program Managers, or Process Improvement Leads. Individually they have areas of specialty, but their impact transcends their specific roles. Operators can get hard things done with few resources, but ironically, because of this, their plates quickly fill up with tedious make-work. This means less time is spent on work that helps fuel growth and innovation. ## **Why companies need Operators** Operators are required to solve thousands of different problems. For example: - If you employ a lot of people, you probably have People Ops challenges involving wage and tax calculations in different jurisdictions. - If you spend a lot of money on marketing, you probably also have Marketing Ops challenges around figuring exactly which platform and campaign brought you every customer. Companies tend to solve problems by doing one of two things: devoting people specifically to that problem or hiring more engineers and data scientists. The first solution doesn’t work because it leads to siloed knowledge and burnout. The second one doesn’t work because while engineers may be great at building solutions that are rock solid, they aren’t as equipped to solve problems for dynamic use cases. In fact, there are plenty of problems that Operators excel at solving that engineers are less equipped for. These problems are generally unique to the company, are fairly complicated, and change often. But when Operators rely on manual processes to solve problems, the solutions become difficult, if not impossible to scale. This leads us to the big problem Operators tend to wrestle with. ## **The problem for Operators** The big problem within Ops teams is entropy: Manual systems do what they need to, but they eventually begin to break down. The usual trajectory looks like this: - Some process within the business breaks, and a team is created to fix it. - Despite few resources, Operators devise a solution with ingenuity and elbow grease. They create a plan to maintain the solution process ongoing, which they do manually. - Months go by…one or two of them leave the company, probably because their weeks are now full of manual work. The company trains replacements, but knowledge is lost in transition. - Eventually something changes, the process breaks, and so the cycle restarts. ## **How Operators should work** Operators should work under four principles: 1. **Automate**: The team should streamline repetitive processes in order to cut down on time and effort and minimize errors. 1. **Centralize**: The data the team uses should pass through a centralized place in a [standardized](https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data) way so that everyone has access to the same information. 1. **Document**: The team shouldn’t perform any process without documenting what it is, who it’s for, and why they’re doing it. 1. **Collaborate**: The team should foster a culture of collaboration and take advantage of centralized data by working together in one place. When these pillars are in place, the general trajectory of Ops teams moves toward order instead of chaos.****The queue of repetitive daily tasks is emptied. Everything that previously took time to slog through is now handled, so a baseline of productivity is established. Without adding any more people, the rate of productivity compounds, and the nature of the team begins to transform. ## **What Operators can become** With compounding productivity, a team of Operators becomes a revenue driver rather than a cost center. This can transform a business. As your team’s rate of productivity increases, the way you think of Ops will change. Instead of simply *keeping things running* so that the tech team or marketing team can materially advance the agenda of the company, Ops teams will yield compounding benefits to the bottom line. Your team will be more able and willing to collaborate with data from a single source of truth, and good ideas will begin to bubble up from team members all over your business. Because you have eliminated the thoughtless daily work that took hours out of their day, Ops teams will be empowered to make the meaningful impact on the business that they have been wanting to all along. ## **How we empower Operators** Parabola builds for the four principles of successful Ops teams. To read about how it works, see our Guide to Parabola: 1. **Automate**: We’ve built a system that can automate any data workflow, no matter what data you start with. 1. **Centralize**: There’s a central hub for Operators and IT teams to collaborate and [standardize data](https://parabola.io/product/transform/standardize-with-ai). IT leaders don’t have to worry about data provenance, and Operators get quick access to what they need. 1. **Document**: Any data workflow created within the Parabola tool is self-documenting — anyone can see exactly what happens to the data at every step. 1. **Collaborate**: In Parabola, you can create [interactive reports](https://parabola.io/product/overview/creating-interactive-reports) in Flow using a [Parabola Table](https://parabola.io/) and share templates and workflows across your teams. ## In summary: Here’s how Parabola empowers operators with automation **‍**Operators across industries rely on automation to streamline workflows, eliminate manual tasks, and drive efficiency. Here’s how Parabola helps automate critical operations for operations leaders everywhere: #### 1. Enhance supply chain automation **‍**Parabola simplifies supply chain automation by integrating data from multiple sources, from [automating inventory updates](https://parabola.io/use-cases/consolidated-inventory-reporting), to optimizing order fulfillment workflows, and more—saving time and reducing errors. #### 2. Streamline logistics automation **‍**Automating logistics automation tasks like [shipment tracking](https://parabola.io/use-cases/tracking-inbound-freight), c[arrier rate comparisons](https://parabola.io/use-cases/carrier-scorecard-reporting), and [freight auditing](https://parabola.io/use-cases/parcel-invoice-audit) ensures smoother operations. Parabola helps eliminate bottlenecks and improve visibility across logistics networks. #### 3. Save time with document automation **‍**Manually processing invoices, purchase orders, or shipping documents slows down operations. With Parabola’s document automation capabilities, you can [parse (pdf parsing)](https://parabola.io/blog/parsing-pdfs-with-parabola), extract, process, and organize information in real time. #### 4. Leverage workflow automation software for scalability **‍**Parabola’s workflow automation software enables teams to create custom workflows that handle complex data tasks. Automate repetitive processes and ensure consistency without relying on manual intervention. #### 5. Boost efficiency with AI workflow automation **‍**By incorporating AI workflow automation, Parabola helps teams make data-driven decisions faster. Automate processes that traditionally require manual input and improve operational accuracy. Parabola is the go-to platform for operators looking to streamline supply chain automation, enhance document automation, and integrate powerful workflow automation software into their daily operations. Whether you need AI workflow automation or a reliable workflow automation tool, Parabola empowers you to work smarter and scale effortlessly. --- # Tracking In-Transit Inventory: Improve Visibility and Gain Real-Time Insights Source: https://parabola.io/blog/track-in-transit-inventory Tracking in-transit inventory is difficult for any Ecommerce business, but Parabola automates this process easily. Read how below. ### What is in-transit inventory? In-transit inventory, also known as pipeline inventory, refers to inventory that has been purchased and is en route to a physical store, warehouse, or distribution center. Though this inventory hasn’t been received at a physical location, it is still included as part of a brand’s overall inventory levels. A miscalculation of in-transit inventory can be disastrous when looking for an accurate picture of your company’s cash flow, planning out storage space, or making sales. ### Why is in-transit inventory important? Teams need to be able to have the most up to date information about their inventory levels. This is helpful across multiple teams: from logistics teams that are responsible for making sure inventory gets from point A to B, to retail teams needing to know when they are getting merchandise in their stores. Having a consistent pulse on your in-transit inventory allows your teams to plan and execute effectively. ### What makes tracking in-transit inventory difficult? If you’re the person at your company that tracks in-transit inventory, you’re probably already painfully aware of these challenges. This is what we hear most frequently from our customers: #### Important data lives in multiple locations When tracking in-transit inventory, your team may find themselves having to use multiple tools to access information that is stored across various files and locations. If you’re working with multiple suppliers, that adds an extra level of complexity to the already time consuming task of tracking data with shipping partners and warehouses. #### Data is presented differently across partners and tools Even after accessing all of your data, each supplier may format their reporting and tracking information differently. The manual process you take to clean data may be unique for each supplier you have. This is not only time consuming but gets difficult to maintain as you scale your operation. #### Inventory data is always updating Long after the workday ends for members of your team, inventory continues to move as it makes its way to you from your suppliers. As shipping containers cross oceans and travel through the air, mechanical issues or inclement weather may cause unexpected delays. With inventory continuously moving and data constantly needing to be updated, it can be easy for information to slip through the cracks. It is important to have a clear, accurate picture of your inventory at all times to maintain a sustainable supply chain. However, keeping this information up to date can be be a significant resource drain, especially for leaner teams. Tracking in-transit inventory is a critical component across all parts of the business. It allows teams to make sure they’re managing their bottom line effectively by not overspending on shipping and meeting their fulfillment goals. Having an up to date view of in-transit inventory allows companies to make better business decisions. ### How can Parabola help? To begin accurately tracking and actioning off of your in-transit inventory data, you first need to create a single source of truth for your inventory across multiple suppliers and freight forwarders. Parabola makes it easy to access data from wherever they exist, whether it be in [email attachments](https://parabola.io/product/integration/email-attachment), static CSV files, Dropbox folders, APIs, or FTP servers. In the example below, one of our customers is bringing in distinct CSV files from three different factories and three different freight forwarders. They normalize these distinct CSV files until they’re ready to be stacked together. They also reference a CSV file of product information to be able to have all important SKU information alongside the data from their factories and freight forwarders. Next, it’s time to clean and format this combined data. This customer is using transform steps like filtering rows, selecting columns, and renaming columns. No matter how you need to transform your data, Parabola’s pre-built transform steps can accomplish it. Not only will this Flow automatically transform the data to your specifications, but this Flow itself becomes a valuable documentation of your process. If one of your source files changes or if the output format needs to be updated, you can easily make adjustments to the Flow. Finally, the data needs somewhere to go to be readily accessed by the rest of your company. This particular brand needed two outputs: both to Google Sheet files that the team could already access. The top branch updates an inventory tracker that shows the shipped quantity and expected delivery date per SKU. The bottom branch updates a summary report that sums the shipped quantity of all SKUs included in a single container. Here’s what this customer’s entire in-transit inventory Flow looks like. This Flow helps them consolidate data from their factories and freight forwarders in a single place, clean and format in a way that serves the needs of the business, and updates this data on a regularly scheduled cadence in a tool that the rest of the team is already using. [Twillory](https://www.twillory.com/) uses Parabola to give the rest of their company a better, more digestible view of their in-transit inventory. Before Parabola, only their Logistics Team was able to make sense of the expected delivery times of the various SKUs because of their deep familiarity with their inventory process and data. Their Parabola Flow now automatically updates a Trello board that their Customer Support Team utilizes when helping customers with questions about expected order delivery dates. Their customer support representatives can easily look up specific SKUs and give their customers accurate information about their order delivery date. This improvement resulted in a happier team and happier customers! ## How to automate in-transit inventory tracking with Parabola **‍**Keeping visibility over [in-transit inventory](https://parabola.io/use-cases/track-and-trace-update-requests) is essential for preventing stockouts and delays. Here’s how Parabola helps businesses improve inventory tracking with automation: #### 1. Ensure real-time visibility into inventory in transit Stop relying on manual updates. Parabola acts as an automated inventory tracking system, keeping shipment statuses and stock levels up to date across all locations. #### 2. Eliminate manual data entry with seamless tracking Instead of updating spreadsheets, automate data collection from carriers, warehouses, and sales channels with Parabola’s inventory tracking solutions. #### 3. Integrate with existing inventory tracking systems Whether you use an inventory tracking software, inventory tracking system software, or ERP, Parabola connects to your tools to keep inventory data accurate. #### 4. Improve forecasting and inventory planning Gain better insights into supply chain movement with an inventory management and tracking system that updates automatically as shipments move through the pipeline. #### 5. Reduce errors and improve operational efficiency Automating your inventory tracking program eliminates delays, prevents overselling, and ensures inventory records are always accurate. --- # Podcast Episode: Unlocking Ecommerce Productivity Through Automation Source: https://parabola.io/blog/podcast-episode-unlocking-ecommerce-productivity-through-automation Our CEO, [Alex Yaseen](https://parabola.io/), joined Shopify Expert Steve Hutt's eCommerce Fastlane podcast to talk about how Shopify stores such as Volcom, Great Jones, and [UMZU](https://parabola.io/customers) are unlocking new levels of productivity through data automation. Alex also shares details about the company’s history, common Shopify use cases, and how automation can benefit ecommerce operations in ways you might not expect. Listen and learn how you can automate manual and repetitive data tasks for your Shopify business with Parabola in Steve’s latest episode: https://ecommercefastlane.com/podcast/episode-146/ If you’re interested in building similar Flows to help your team with [inventory forecasting](https://parabola.io/use-cases/inventory-reconciliation), you can start building your own Flow for free by signing up for Parabola. Email us at [help@parabola.io](mailto:help@parabola.io) and we can help you get started! --- # Starter-pack Parabola Templates for Ecommerce Brands Source: https://parabola.io/blog/matt-hertzs-starter-pack-for-ecommerce-brands I'm [Matt Hertz](https://parabola.io/) and I'm the co-founder of [Second Marathon](https://secondmarathon.com/), where we work with ecommerce brands to develop and improve their shipping and fulfillment operations. We've supported some of the leading ecommerce brands, such as Haus, Walmart, Away, and Birdies, and I previously led operations at Rent the Runway, Birchbox, and Shyp. Across the work I've done, I know how difficult it can be to stay on top of shipping and fulfillment metrics. And to date, the best way to do so has been lots of manual work in Excel or Google Sheets. When you want to refresh the data, you download and cobble together all of those CSVs and do the same repetitive work each day/week or whatever time frame you're working with. I got crazy excited when I heard about what Parabola is doing. I recently played around with their new [ShipStation integration](https://parabola.io/product/integration/shipstation) and wanted to share a starter pack of recipes to get you started. Typically, when we work with customers, we export data from tools like ShipStation, bring it into Excel or Google Sheets, and manually work that into a report. What got me so excited about Parabola was that all we needed to do is set these flows up once and now all my clients have to do is connect their ShipStation accounts and can get up and running. Instead of repeating the same analysis in a few months, you can run these flows on a schedule so you're constantly getting up-to-date metrics. I hope these can help you stay on top of your shipping and fulfillment metrics. Here's an example of one of the recipes, and you can see the rest below! #### More recipes - ‍[Weekly shipping KPIs email summary](https://parabola.io/recipes/weekly-shipping-kpis-summary) - [Shipping volume by carrier weekly summary](https://parabola.io/recipes/shipping-volume-by-carrier) - [Shipping spend by carrier weekly summary](https://parabola.io/recipes/shipping-spend-by-carrier-weekly-summary) - [Shipping volume by weight (distribution) weekly summary](https://parabola.io/recipes/shipping-volume-by-weight-weekly-summary) - [Shipping volume by carrier/service weekly summary](https://parabola.io/recipes/shipping-volume-by-carrier-service-weekly-summary) - [Shipping volume by state](https://parabola.io/recipes/shipping-volume-by-state-weekly-summary) Feel free to shoot us a note if you have any questions: hello@secondmarathon.com, or sign up for Parabola today for free! --- # Automatically Send a Valentine's Day Wish Source: https://parabola.io/blog/automatically-send-a-valentines-day-wish #### Roses are red, Parabola's blue. Automate your work day, and your Valentines too. Happy Valentine's Day, Singles Day, Galentine's Day, or Singles Awareness Day from all of us at Parabola! Whatever you're celebrating, it's always great to send a note to friends and family and let them know that you're thinking of them. However, who really has time for all that between all of the chocolate eating, Netflix binging, and champagne sipping? Instead, do it automatically with Parabola. Sending one-off emails is a thing of the past. Now, you can message your entire contacts lists all at once and get back to making heart-shaped pizzas or streaming hours of The Office on Netflix one last time before it's pulled from the service. We won't judge, especially if you're up to both. So, do you need more time in your day for yourself or that special someone? Well, get it back by using this Valentine's Day recipe below. There's no better gift! And, who knows? You may also make someone's day! ‍ [Try the recipe out for yourself by clicking here.](https://parabola.io/api/clipboard/5f875dc821c04cc1a03f2fda7a8c1f13/copy_to_flow?name=Happy+Valentines+Day) For dozens of other Parabola ideas, check out our [Recipes page](https://www.parabola.io/recipes)! --- # Learn how to work with APIs Source: https://parabola.io/blog/how-to-work-with-apis Many of our best users leverage APIs using our [API Import](https://parabola.io/), [API Enrichment](https://parabola.io/), and [API Export](https://parabola.io/) steps. Through those steps you can connect to virtually any API across the web and import or export whatever data is needed. However, if you're relatively new to working with APIs, trying to read their docs and understand which endpoints to connect to can be pretty tough. We realized there weren't many good resources online about how to do this, so we put together a friendly guide! Through the guide, we'll walk you through how to read API docs and use the data coming back from them. We brought along a friend, [Slash the API dog](https://slashtheapidog.com/), who has his own example API to make each API related action easier to grasp and hopefully a bit fun! ### [Read the full guide here at WorkWithAPIs.com](https://workwithapis.com/) ‍ --- # Give Time Back to your Sales Team with Codeless Automation Source: https://parabola.io/blog/give-time-back-to-your-sales-team-with-codeless-automation If you’re a sales leader or SDR, you’ve felt the soul-crushing time drain of maintaining and updating Salesforce or another CRM with lead status, communication channels, profile data, and so much more. It’s excruciating. Add in the frequently manual process of compiling reports on a regular basis, and it’s no wonder why sales teams spend a good chunk of their quarter not selling. **In fact, sales teams, on average,**[spend almost a third of their time](https://blog.hubspot.com/sales/salespeople-only-spent-one-third-of-their-time-selling-last-year)**updating CRMs, managing data, and creating reports**. That’s time that could have been spent prospecting, contacting leads, giving demos, or closing deals. Your sales team spends one-third of their time not selling. Even if you’re lucky and your sales tools already have integrations with your CRM, **data frequently doesn’t quite line up**. Reports for a specific metric never seem to have the right data. To make matters worse, if you want to automate anything in Salesforce, you are going to need a consulting agency *(read: far too expensive for what you get)* or dedicated engineering time *(read: a waste of resources)*. We feel your pain, and that’s why we’ve built Parabola. [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Sales_Automation&utm_campaign=DemandGen) enables *anyone* to work with and automate their data as effectively as an engineer without needing spreadsheets or code. Quickly and easily connect to any data, no engineers required With the increasing number of tools utilized by most sales teams, it’s becoming common to have data that is incomplete, incompatible, or inconsistent. Trying to connect disparate date is usually a task limited to overworked development teams. [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Sales_Automation&utm_campaign=DemandGen)****has an ever growing list of integrations *(including common databases)* that allow you to pull in data from anywhere in a few clicks, without any code required. Pull in data from anywhere in a few clicks, without code. **With data integrations and automation, you can:** - Join **Google Analytics** data with **Stripe** data, to see which blog articles bring in the most enterprise customers. - Connect **Mailchimp** campaign data with both **Salesforce** and analytics data stored in **Redshift** to get a complete picture of what a user is doing on and off your site, before trying to up-sell them. - Use custom logic and references to data from other services to automatically update **Salesforce** accounts and leads. - Create a **shared data pipeline** that can be used by multiple users for reporting, analysis, or normalization. - Send out **live sales reports** synthesized from multiple sources to your team. - Automatically clean new data before it enters your **CRM.** - **Update Salesforce leads** to Do Not Contact using the results from an opt-out link in automatic emails, or from a form on your site. - **Update a lead to an opportunity automatically** when a lead performs some sort of action visible in analytics, or through a form. - Check leads without Opportunities Set against your new contacts, to catch inconsistencies. Then **automatically update the leads** to have Opportunities Set. - Automatically update Salesforce via **Google Forms** used by AEs and SDRs for**quick data entry.** Reclaim your day with automation Most of the hassle of a powerful CRM like Salesforce comes from keeping records up-to-date. Why are you spending *a third of your week* updating Salesforce and generating custom reports? Automation takes care of the menial tasks that feel manual and slow, and gets a team back to doing what they do best — selling. Using****[Parabola’s](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Sales_Automation&utm_campaign=DemandGen) Automation, you can recapture enough time to help your sales team blow past quota and still make it to happy hour. [Use Parabola to Automate your manual sales data tasks today](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Sales_Automation&utm_campaign=DemandGen) ‍ --- # Automating the Rest of Your Marketing Pipeline Source: https://parabola.io/blog/automating-the-rest-of-your-marketing-pipeline When it comes to automated email marketing, the underlying data is directly tied to a campaign’s success. With good data, services like Mailchimp, Active Campaign, Hubspot, Marketo, and others can do a great job of sending personalized marketing emails to targeted consumers. The power of good email marketing automation lies not in the volume or scheduling of the emails, but in the level of testing, iteration, and ultimately personalization. In order for email marketing to be successful, you need to: - Collect and understand the data to be used for personalization - Understand and measure the goal of each campaign Then you can **build an automatic email marketing machine** that reaches qualified leads and **delivers personalized messages that resonate.** This is how you can quickly expand your marketing efforts and see an **instant return**. [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email_Marketing_Automation&utm_campaign=DemandGen)****drastically speeds up and simplifies this data legwork. Here are the steps to deal with multiple data sources, automate your data management, and track custom marketing goals that update your strategy: ‍ 1. Collect and unify your data Collecting raw data for automated email marketing is easy. You probably have any number of the following: - A [lead magnet](http://www.investopedia.com/terms/l/lead-magnet.asp) on your site to draw in new visitors and passively collects emails. - A lead chat solution like Intercom to collect pieces of data about leads as they talk to you. - An email newsletter or blog sign up form - An integration with Clearbit to deliver data enrichment - A CRM like Salesforce containing a table of leads and their statuses - All of this data lives in separate locations, each with its own unique environment and set of rules. This data becomes a powerful marketing asset when you combine it into a complete data picture. - Using [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen), anyone can import and combine data from any of these sources. [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen) then enables anyone to clean, dedupe, merge, filter, enrich, and add logic to the data, all without touching a line of code or dealing with a fragile spreadsheet. - For example, with [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen), you could automate **a retargeting campaign** **for leads** who previously downloaded your lead magnet, but never converted: - Import your Salesforce leads - Filter down to those who have not converted in the last 60 days - Import your lead magnet email list - Join the two lists, only keeping those who who exist in both - Import your Clearbit data for those emails - Use that to add in names, company sizes, industry, and more - Export that data to your email campaign tool [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen) has created a list of people to retarget and some information to use in the email personalization. Generally, using first names (instead of full names) increases open rates, so use the [Name Parser Object](https://parabola.io/recipes/separating-name-fields?utm_source=Medium&utm_medium=Article&utm_term=Email_Marketing_Automation&utm_campaign=DemandGen) to pull out first names. ‍ ‍ 2. Automate the data Unified data can be used to create personalized emails that will attract and convert leads. You could repeat the aforementioned steps with each segment of leads that you are targeting, but that could take hours to pull each new data set and run the flow manually. Your time should be spent on analyzing, testing, and iterating, so **why not automate the data processing?** Once a flow is set up,[schedule Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen) to automatically refresh the data coming in and then pass it through the transformations in your flow. Set it to run automatically every day, or just when you have a new campaign to send out. **The data always stays fresh.** 3. Take your personalization to the next level Basic company data is not that impressive for automated email marketing. How about sending to multiple email addresses, some who are CC’ed, and having the salutation automatically include all the first names, with proper punctuation? **That would be far too time consuming to do manually without**[Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen). For example, when marketing a service that charges per seat — it would be common to target adjacent employees in the company who are not yet using your service. Obviously, a hand-typed email would be best, but sales and marketing superstars don’t have time for that. Use a combination of [data manipulation objects](https://parabola.io/transformations?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen) to build a flow that will combine the first names of all of the recipients at a company, join them with commas, and insert an “and” before the final name. Even add “(CC’ed)” after each name whose email is in the CC column of the table. **This can be done automatically and intelligently**, checking if there are leads who are not closed, and who have colleagues already using the service. When someone’s colleague signs up for the newsletter months from now, they will receive an extremely personalized email — automatically. 4. Custom goal tracking without an engineer Most marketing automation tools have some form of goal tracking feature, but let’s be honest, **it never works like you want it to**. Many times the utility of goals is limited by the tool. **To maximize your efficacy, you need custom goals**. Ty to set a goal that x% of the people who receive the campaign should sign up on a business-tier plan within 30 days, and they should invite at least one colleague to join them. How could that goal be set without involving an engineer? With [Parabola](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen) create this complex goal by simply branching off of the flow, and adding in a few more data sources: *Data from Salesforce on who has invited users* *Data from Salesforce on who is on which plan* Take that data and do the following: - Join it with the existing table of leads who received the campaign - Filter it for leads who are still within the 30 day success window - Total it up The results are custom goal success metrics. You could even plot them on a chart to track performance over time. Automatic email marketing is a foundation for growth Companies grow by generating continuous demand and then capturing and nurturing leads. Adding automation and improving the efficiency of the top of the funnel is a great way to grow your business while simultaneously freeing yourself up to [tweak](https://parabola.io/recipes/summarize-data?utm_source=Medium&utm_medium=Article&utm_term=Email_Marketing_Automation&utm_campaign=DemandGen)****[other](https://parabola.io/recipes/if-else?utm_source=Medium&utm_medium=Article&utm_term=Email_Marketing_Automation&utm_campaign=DemandGen)****[areas](https://parabola.io/recipes/phone-number-cleaning?utm_source=Medium&utm_medium=Article&utm_term=Email_Marketing_Automation&utm_campaign=DemandGen)****of your sales and marketing engine. ☞ [Improve your marketing automation with Parabola today](https://parabola.io/?utm_source=Medium&utm_medium=Article&utm_term=Email+Marketing+Automation&utm_campaign=DemandGen)☜ ‍ --- # How Bandit’s ops team reclaimed 10+ hours a week & built real-time visibility with Parabola Source: https://parabola.io/customers/bandit ## A fast-growing brand with no time to waste Bandit Running is a fast-growing performance and lifestyle brand based in New York. Known for its limited-run product drops and clean, elevated design, the team has built a loyal following across ecommerce, retail, and pop-up events. But scaling that kind of complexity with a small operations team is no easy task. “We’re a startup. We’re scrappy. And we’re growing fast,” says Emily Shaw, Head of Operations. “That means flying by the seat of your pants sometimes, but you still need clean, reliable metrics to actually understand what’s happening.” As Bandit scaled, the operations team needed a way to rethink and evolve how they tracked performance, surfaced issues, and reported out across the business. Their metrics had to keep pace with constant changes: new systems, more data, and shifting workflows. “The challenge was pulling together data from all the different tools we use—Shopify, ShipHero, Gorgias—and making sense of it all without spending hours every week doing it manually,” Emily says. They needed a better way to centralize data, adapt to change, and build confidence in what they were measuring, all without hiring a full data team. ## Workflows built without a data team Instead of relying on manual workarounds or expanding headcount, the team turned to Parabola. What started as a conversation at an operators community dinner turned into a product that’s now central to how Bandit’s ops team works. “Parabola lets us combine data across our tools and build the workflows we need to actually see what’s happening and act on it,” Emily explains. The team built several flows that now power their day-to-day operations: - **Warehouse tracking**: Data from their 3PL and Shopify is pulled hourly to monitor fulfillment performance and surface any orders that fall outside of SLAs - **Issue alerts**: Each morning, Parabola flags orders with potential issues so the team can jump in and resolve them quickly - **Customer service reporting**: Ticket data from Gorgias is categorized and tracked as a percentage of orders, helping the team identify trends, top contact reasons, and areas for improvement - **Weekly metrics review**: Over 30 key metrics are now automatically compiled and validated each week, replacing hours of manual reporting > Could we have done some of this before? Definitely. But it would’ve taken more time, and we wouldn’t have had the same confidence in the data. Now we can report out quickly, track performance from each product drop, and actually use that data to make decisions.” The flexibility of the product has made it easy for the team to evolve as the business grows. “As our business changes, we’ve been able to update what we’re tracking and how.” ## Real-time insights, fewer surprises With Parabola, Bandit’s operations team has been able to keep up with the business’s rapid growth without adding more complexity. - **10+ hours saved each week on reporting:** Weekly metric reviews that used to take hours are now compiled and validated automatically - **Faster issue resolution through proactive alerts:** Daily Slack alerts flag order issues so the team can resolve them before they impact customers - **More accurate fulfillment tracking:** Real-time data from Shopify and their 3PL helps the team monitor SLA performance with confidence - **Deeper visibility into customer trends:** Support tickets are tracked as a percentage of orders, helping the team identify common issues and reduce contact rates - **Greater agility without engineering resources:** The ops team can connect data and build what they need without relying on technical headcount “Having that kind of real-time visibility means we’re not just reacting anymore. We’re catching issues early and making smarter decisions,” Emily says. She also credits Parabola’s team for helping them get there. > The support we’ve gotten has been unmatched. Between onboarding and all the new capabilities like custom transforms and AI-assisted building, it’s made it easy to keep improving and stay agile.” “We’ve got a lot more we want to build. And we feel confident knowing we’ve got a partner that’s growing with us.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Blenders uses Parabola and Flexport to bring clarity to a complex supply chain Source: https://parabola.io/customers/blenders ## Manual work, mismatched systems, and month-end surprises [Michael Villa](https://www.linkedin.com/in/villamichael/), Director of Operations at Blenders, leads a lean team managing logistics, inventory, and planning. As the company scaled, they turned to [Flexport](https://www.flexport.com/) to modernize their fulfillment operations. The physical operations ran smoothly and efficiently, but as they scaled, reconciling data across multiple platforms across their business became challenging. “We’d get to the end of the month and find ourselves inexplicably misaligned by hundreds of units across dozens of SKUs,” Villa said. “And the more we tugged on the thread, the less sense things made.” Blenders was managing inventory across NetSuite, Shopify, and Flexport, but aligning those systems proved tough. Time zone mismatches, internal transfers, and the limits of prebuilt reports created gaps that made it difficult to understand what was actually happening. Michael needed more visibility across Blenders’ full stack. ## A flexible layer to make sense of it all Instead of replacing what was already working, Michael brought in Parabola to help connect the dots. By pulling in data from NetSuite, Shopify, and Flexport, the team built daily flows that flagged exceptions, reconciled records, and gave them a clearer picture of what was happening. > You don’t have to be a tech expert to use Parabola. I just rolled up my sleeves and started connecting the dots. It helped me make sense of all the data across our systems—without needing to be an engineer.” One of the first breakthroughs came from normalizing time zone data across reporting platforms. That small detail was behind some of the biggest reporting gaps. From there, the team kept building. They layered in logic, filtered down to just the data that mattered, and started surfacing issues before they snowballed. Instead of spending hours chasing down errors, Michael’s team now spots and resolves issues before they become problems. Flexport stayed closely involved throughout. “They were super supportive,” Michael said. “Once a few key folks were looped in, it really became a collaborative effort to make sure the right data was flowing and the workflows were working.” ## Less scrambling, more confidence, and a foundation for growth Blenders now has a reconciliation process they can rely on, and time back to focus on higher-leverage work. - **Time savings:**Inventory reconciliation dropped from 1–4 hours per day to just 1–2 hours per week - **Improved visibility:**Reports that were previously reviewed weekly or monthly are now monitored daily - **New workflows:**Parabola powers demand planning support, open order audits, and cross-system checks - **Stronger alignment:**Finance and ops work from the same data, so surprises are rare > The real ROI isn’t just the hours we save—it’s the mindset change. What used to feel like an impossible mountain of data is now a daily routine. We drop it into Parabola, build a new flow, and let it flag what matters. Suddenly we’re tackling projects we never dreamed of doing every day, and the team feels unstoppable.” Blenders can now plan faster, move with confidence, and turn once-daunting data challenges into everyday wins. *—‍This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # Caraway Home gets 150 hrs/month back by automating their supply chain ops Source: https://parabola.io/customers/caraway ## Tons of manual processes limiting growth As the Associate Director of Logistics and Fulfillment Operations at Caraway, Lotzof has a huge scope. Her purview includes managing inbound and outbound logistics, 3PL relationships, dropship wholesale orders, Amazon business operations, cross-functional forecasting, and freight forwarding. Across all of those areas of responsibility, Lotzof reported inefficiencies that were creating bottlenecks. Her team was performing manual landed cost calculations, didn’t have real-time visibility into inventory across sales channels, struggled to be proactive when it came to shipping issues — and Lotzof was worried about the inevitable human errors that arise when dealing with huge sums of data manually. > Automation is one of the key things I have been focusing on since I started working at Caraway, so I knew this was a good time to capitalize on some automation opportunities.” ###### Katya Lotzof, Associate Director of Logistics and Fulfillment Operations She knew they needed to find a solution. “When you grow, tiny issues become big issues,” Lotzof told Parabola. “And you can’t just keep adding manual work. Automation is one of the key things I have been focusing on since I started working at Caraway, so I knew this was a good time to capitalize on some automation opportunities.” ## End-to-end automated operations If there’s one thing to know about Lotzof, it’s that if there’s something to automate, she will automate it. She has an amazing capacity to identify areas where automation will make the biggest impact, and Parabola has given her the tools necessary to actually realize her vision…and her vision was clear from the start. Before we had a call, the team sent me demo videos and training documentation. When we started our conversation, I already knew the terminology — I knew what the Canvas was and generally how building worked,” says Lotzof. “It made it easier for me to understand what’s happening in Parabola.” And it also made it easier for her to immediately solve one of their most frustrating problems: Processing landed cost. ### Landed cost automation Processing landed cost is notoriously difficult and time consuming. For brands to fully understand the total cost of a product once it reaches its final destination requires incorporating data from multiple sources — and it isn’t uncommon for this data to come in unstructured, difficult-to-work with formats like PDFs. For Lotzof, this process required getting invoice data out of PDFs that were emailed to her and copy-pasting them into a template that would flow into Fulfil (their ERP) where landed cost was calculated. “It was very time consuming,” she told me. Here’s how Parabola completely eliminates that work: 1. The Flow automatically processes invoice PDFs that come in through email 1. The **Extract with AI** step identifies the invoice type (whether it’s core, accessorial, credit memo, etc.) 1. It then reformats the data to match the ERP requirements 1. And populates a standardized template, ready to import Once Lotzof got this Flow up and running, she was hooked. Here’s what else the team built: ### Pre-transit monitoring Next, the team tackled automated pre-transit monitoring, which is the process of identifying shipments that haven’t shown tracking activity for a certain amount of time. Having visibility into this allows Caraway to get proactive with customer service if a shipment is held up — and it improves customer experience through faster issue resolution. Before building this Flow, the Caraway team would pull manual reports from EasyPost Monday, Wednesday, and Friday with each report taking about one hour to pull and format. With Parabola, the Flow monitors tracking data from EasyPost, automatically identifies shipments without movement, generates reports on that data, and surfaces it to the right people so they can determine next steps. Collecting this information used to take three hours a week — now it happens all of the time and is completely automated. ### Inventory position dashboard “Another amazing Flow that is loved by many teams is our inventory position dashboard,” says Lotzof. The team at Caraway created a Google Sheet that’s updated using Parabola that includes: Inventory positions for all SKUs per location, dedicated tabs for bundles and BOMs, and upcoming inventory arrivals with units. Pre-Parabola, they ran into issues with a similar sheet that was manually updated by humans. Lotzof explained the pain: “You know if there is a Google Sheet there are multiple owners, and there might be human errors that affect the data. Sometimes the formula is overwritten or things get inputted incorrectly. Parabola helps to resolve this issue because the data is updated through the platform.” This has been a huge value add for Caraway and for the ops team’s cross-functional relationships. Teams can self-serve inventory information, there’s increased visibility into overstocked and out-of-stock positions, and future inventory plans are more clear. ### End-of-month reporting Before Parabola, end-of-month reporting was a huge hassle. The Caraway team had to pull multiple reports from their ERP system, and were always up against a limitation of 20,000 entries per export. The process could take up to 12 hours over a few days during peak season, and they often had to pull up several reports. When the team felt particularly under water, they’d rely on their ERP partner to pull reports for them due to how manual and time-consuming the process was. With Parabola, the process now takes about 30 minutes. In Parabola: 1. Reports are automatically ingested into Parabola, eliminating the need for manual data pulls 1. Parabola performs all of the data consolidation and lookups the team was performing by hand before 1. They have constant insight into their data instead of relying on a once-monthly pull from the ops team ## “Hours have turned into minutes.” Across the Flows that Caraway has built, they’ve realized immense value. To name a few highlights, Caraway has: - Decreased peak season end-of-month reporting from 12 hours to 30 minutes - Eliminated the need to spend an hour on pre-transit reports — these reports now happen automatically, in real time - Cut landed cost processing time from 1.5 hours to 10 minutes - Improved data accuracy across the board To put it in Lotzof’s words: “Hours have turned into minutes.” Outside of the very clear quantitative impact Parabola has had for the Caraway team, Lotzof also mentioned something that could arguably be even more impactful: Team morale is up and to the right. > Parabola has eliminated tons and tons of manual steps, it saves hours of work on a weekly basis, and it also helps eliminate human errors. And because my team is not working on these manual, repetitive steps, they can focus on something else. Their morale is actually improving because they can work on something interesting and exciting rather than boring and repetitive.” ###### Katya Lotzof, Associate Director of Logistics and Fulfillment Operations Lotzof’s laser focus on automation has definitely paid off for Caraway and her team — and it’s created an absolute Parabola power user who is always looking for what’s next in the process. When I asked Lotzof how she thinks her team would do if their Flows got ripped up tomorrow, she said “When you’re spoiled with something amazing and it’s taken away from you, you’d treasure it a lot more. We would be able to manage, but it would definitely add back many hours of work — and some things would have to fall through the cracks.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Coterie saves at least $150k a year and scales without adding headcount Source: https://parabola.io/customers/coterie ## The reality of scaling a bulky product For Coterie, running a successful operation requires orchestrating multiple complex elements while maintaining perfect reliability for subscribers. Managing everything from strategic sourcing to customer experience—with shipping costs being a crucial factor—means every aspect of their operation needs to work in harmony. > So much of our cost is centered around moving our product,” Zachs explains. “Diapers are big and bulky, and it’s costly to both freight it into our warehouses and ship it outbound to our customers…shipping costs dictate our supply chain network. The most obvious way to expand our gross margin is almost always either freight or parcel.” As VP of Ops, Zachs leads a remote-first team handling strategic sourcing, vendor management, planning, inventory, transportation, fulfillment, customer shipping, and customer experience. Each of these areas requires constant data analysis and optimization to maintain efficiency. “We run a very lean team at Coterie,” Zachs notes. “And in the past two years more than 60% of my team, including myself, have taken parental leave. Working for a baby care company comes with really generous and lovely parental leave, but it also means that at a given time, we are doing multiple jobs at once.” Despite her extensive background in consulting and startups, even Zachs was initially skeptical about moving away from traditional tools: “I’ve spent my entire career in Excel and Google sheets, and even when you have full-fledged ERP systems or fancy BI tools, the reality is, there’s just so much that Excel and only Excel and Google sheets can do.” ## Turning complexity into control ### Parcel optimization Their primary parcel automation Flow handles all the fundamentals—auditing base rates, fuel surcharges, dimensional weights, and packaging accuracy—but Coterie has taken it “10 steps further.” The Flow now provides order-level contribution margin analysis by combining parcel data with NetSuite order information. This enables the team to inform their go-to-market strategy by identifying which bundles deliver higher contribution margins. It also quickly pinpoints warehouse inefficiencies in order packing and box sizes, leading to meaningful cost savings. In one instance, they discovered their warehouse wasn’t effectively consolidating multi-case orders, an insight that saved them at least $12,000 per month. ### Freight cost analysis The team built sophisticated Flows to track and optimize freight costs, both domestic and international. By ingesting carrier invoice data through standardized Excel templates, they've created comprehensive views for budgeting, re-forecasting, and carrier negotiations. “Having a really solid understanding of your freight costs and all the individual components beneath that is super critical,” Zachs explains. “You need to have them at your fingertips where before they were buried in invoices, or we would just have to manually track them.” This visibility enabled them to secure fixed rates with freight forwarders and optimize their split between fixed rate and market rate volume. ### Final mile delivery optimization It’s clear that consistent delivery is non-negotiable for a subscription-based diaper company. Coterie’s final mile optimization Flow integrates P44 tracking data to analyze carrier performance at the zip code level, enabling precise cost versus lead time analysis. This granular insight helps them optimize carrier selection between OnTrac, UPS, and FedEx. When they identify performance issues in specific zip codes, they can quickly adjust routing to maintain service levels while controlling costs. ### Try parcel spend forecasting in Parabola The transformation in how the team interacts with data has been dramatic. As Zachs puts it: “You go from manually updating the data to just looking at it…isn’t that a beautiful thing? When I just look at the outputs and it's already clean and done for me.” ## Doing more with less The impact of these automations has been transformative. In the past two years, Coterie has seen significant growth without a corresponding increase in operations headcount. “My team hasn’t grown over the last couple of years, and we’re always down someone at any given time. So not only do we not need to hire, but we're pretty much always in a deficit,” Zachs explains. Despite this lean approach, the team has maintained exceptional operational efficiency. With Parabola, they: - Identified $12k/month in savings by detecting inefficient multi-case order consolidation at warehouses - Helped the business scale >40% YoY without increasing headcount on the supply chain team - Automated 10–30% of each teammate’s role so they can focus on higher-value work - Enabled better freight cost management, supporting fixed rate negotiations and partner QBRs - Optimized parcel carrier selection down to a zip code–level When asked what would happen if Parabola disappeared tomorrow, Zachs’ response is immediate: “There would be a moment where I’d be a little bit paralyzed…my whole team would cry, because we’d have to go back to not only an analog version, but an inferior version.” Perhaps most importantly, the automation has sparked a cultural transformation in how the team approaches problems. > Once you do a couple of steps, you just get it, and then you’re off to the races, and then it just becomes insanely addictive. And it also just changes your mindset of what is possible,” Zachs reflects. The automation has given her team unprecedented visibility and control over their operations. “When you do analysis in Excel, you know that you’re just scraping the surface, and usually it's good enough. But you know you're leaving money on the table because you’re not able to look at the data deep enough to find non-obvious opportunities.” For a company that needs to be there for parents at 2 AM, having this level of operational efficiency isn’t just about margins—it’s about delivering on a promise. Through automation, Coterie has built a well-oiled operation that ensures they’ll always be there when parents need them most, proving that with the right tools, you can scale operations without scaling headcount. — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Faherty scaled order management across 80+ stores with Parabola Source: https://parabola.io/customers/faherty ## Managing orders at scale became unsustainable When Zanie Barksdale first joined Faherty, the brand had 12 retail stores. Seven years later, it has grown to more than 80 locations, with an expanding e-commerce business and wholesale partners. That rapid growth created new challenges for operations. “When I first started, I was managing customer returns across 12 stores. Now we’re at more than 80, and the volume is just on another level,” said Zanie. As Senior Manager of Business Operations, she and her team are responsible for keeping orders moving across Shopify, NetSuite, warehouses, and carriers. “I was constantly reviewing systems, reporting, and making sure all these orders went from this system to this system. It was just wasting my time looking for issues,” said Zanie. The work was manual, repetitive, and left little room for higher-value projects. Errors often went unnoticed until customers reported problems, putting SLAs and satisfaction at risk. ## Building automated flows and leveraging AI for complex challenges Faherty’s IT team introduced Zanie to Parabola, and what started as an experiment quickly became part of daily operations. “At first I felt a little intimidated,” said Zanie. “But after the walkthrough, I realized it was pretty easy and cool that I got to be in charge of what I wanted to set up.” She began by automating simple system checks that flagged missing or exception orders. Instead of spending hours combing through spreadsheets, Parabola ran the pulls automatically and sent her an email when something was wrong. From there, the use cases expanded. Today, Faherty uses Parabola to: - Prepare shipment imports and enforce rules like maximum shipment sizes - Feed clean data directly into NetSuite without manual rework - Proactively flag potential errors before they reach customers Zanie also discovered that Parabola’s AI features could help her solve problems she didn’t know how to approach on her own. “I was really stumped on one of my flows for shipments. I asked exactly what I wanted, and it ended up giving me the exact result I needed,” said Zanie. > That was when I realized Parabola could help me get past roadblocks faster than trial and error ever could.” ## Turning hours of manual work into minutes By making Parabola a core part of daily operations, Faherty’s team has reclaimed hours each week and improved the customer experience. - **5–10 hours saved every week:** Large shipment drops that once took 2+ hours now run in minutes - **Improved SLA performance:** Orders are flagged and fixed in real time, keeping fulfillment within Faherty’s 2-day promise - **Better cross-team collaboration:** Inventory analysts, wholesale ops, and allocation teams share flows, reducing silos and speeding up communication - **Flexibility to pivot quickly:** Custom flows let Zanie adjust reporting or add new data points on demand, without waiting on IT *Zanie and her teammates from the operations crew at Faherty.* ‍ For Zanie, Parabola isn’t just a time-saver. It’s become part of how Faherty operates at scale. > Parabola has been such a huge lifesaver. The first time it might look scary, but once you put the time in and have it set, it literally saves so much time. You’re just taking minutes to run something.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Great Jones uses Parabola to increase their supply chain data visibility Source: https://parabola.io/customers/great-jones ## On-hand inventory reporting to get ahead of stock-outs In Q4 2022, the worst happened: One of Great Jones’s best-selling items (the blue Little Sheet quarter-sheet pan) ran out of stock for some of the holiday season. This resulted in missed sales for the brand, during the most critical sales window of the year. “Shopify has a lot of limitations with their analytics — it’s not always the most user friendly,” McCarthy said. The lack of connected systems and combined data made it hard at any given time to have a full picture of what Great Jones had on hand and what their future demand would look like. This led to the creation of one of their mainstay Flows: an automated inventory on-hand workflow and corresponding dashboard. Here’s what they set up with Parabola: 1. The Flow pulls in current inventory from the WMS, their product catalog including average units sold/day, and inbound inventory. 1. It starts by looking for any low inventory counts
(SKUs with We can see patterns much faster and easier than 
we were able to previously. We have gotten way, way, way better and way more organized simply by just having the Flows tell us exactly when items are going to stock out and how many months out we have.” ###### **Claire McCarthy** Director of Logistics and Operations
at Great Jones ## Automated order management to streamline their engraving workflow A special touch for Dutch ovens created a tedious, manual task for the Great Jones team. Great Jones originally found Parabola in the search for a solution to a pretty simple (but very manual) process. On Dutch ovens that come in colors like Broccoli, Taffy (a perfect bubblegum pink), Blueberry, and Mustard, Great Jones offers an option to add an engraving. With up to 25 characters to spare, a monogram- and soup-obsessed chef can personalize the newest addition to their cooking arsenal. For Great Jones, this meant manual data pulls from Shopify that they’d then send to their third-party engraver (who was also responsible for shipping). Following up for tracking and shipping information from the engraver also fell onto the ops team. This made for a pretty tedious and hard-to-scale process. Great Jones’s first Flow (which is still live today) pulled Shopify data like order info, shipping address, and engraving inscription and flowed it into a Google Sheet for the engraver to use. “But then,” McCarthy said, “it was still a manual process to get the tracking information back into Shopify. We’d just have to do a weekly copy over. About a year later, we built the second half of the flow, which was an automation that pushes the tracking information back into Shopify.” Before implementing Parabola, there were a couple of months that the Great Jones team paused engraving because of the team bandwidth it required, losing out on thousands in revenue. One simple flow allowed them to keep offering a service that delighted customers in a way that was scalable and sustainable for their team. ## Fulfillment scorecarding to improve warehouse SLAs and customer experience With this next Flow, Great Jones was looking for a better understanding of their fulfillment operations. Both directly and indirectly, this has supported goals around overall shipping efficiency, NPS score, return rate, product reviews, SLA compliance, and visibility with their external partners. They start by pulling in order data from Shopify. From there, they manipulate the Flow to calculate the number of days it took to fulfill an order. Then the flow extracts various metrics, like percentage of orders that were shipped on time vs. shipped late. They also calculate damage and error rates by pulling in data from their return management system. One of the main intangible benefits from the Flow is really the ability she and the CX team have to be more proactive. The CX team is enabled to reach out quickly if there are delays to give customers updates. And for McCarthy’s team, it sort of acts as a canary in a coal mine: “If we’re off track and our damage rates or our error rates are high, we know then we need to dig in further to mitigate and fix whatever is going on.” > Our warehouse SLA and customer experience are super dialed. If there’s any issue or any error happening within the order space, we can see that really, really quickly now and that inevitably has an impact on our NPS score, return rate, and product reviews — there’s really a trickle-down effect.” ###### **Claire McCarthy** Director of Logistics and Operations
at Great Jones — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Juice Press saved $200k automating one workflow Source: https://parabola.io/customers/juice-press ## Manual inventory management and tip allocation Before Parabola, Juice Press was manually completing processes across multiple parts of their business, including finance, HR, payroll, and their production team. They were spending hundreds of hours per week on manual, repetitive tasks, ultimately hurting their margins. Two processes in particular were eating up most of this time: - Ariana Korman, Juice Press’s COO, had to manually update inventory and ordering information for each of their 85 store locations. - Each location was separately managing their own tipping pools in a manual process that required HQ to pull four or five reports into their accounting software. Juice Press had a few goals they wanted to achieve with Parabola: - They wanted to get their employees’ time back. They didn’t want to have to continue hiring to keep up these manual processes. - They wanted to improve how these processes were run and get rid of the inefficient Excel tasks. ## Tip allocation automation with Parabola Juice Press was looking for a solution that was customizable, but also powerful enough to automate all of the tasks they needed to optimize. When they started out with Parabola, they set up a few Flows to address some of their most essential workflow inefficiencies. The first of their Flows were meant to automate inefficient Excel processes, like tip allocation. They created a 38-step Flow that pulls in tip data for a specific date range from the point of sale from each store location through a direct integration with Square. It pulls data from their payroll system to determine hours worked by each employee within the same date range and generates a report in a format compatible with their payroll system, allowing them to allocate tips accordingly. They also set up two different Flows to automate ordering inventory for each store location. 1. A Flow was built to create order templates. It pulls every store’s desired (“par”) and actual inventory levels from MarketMan, their restaurant management system, to automatically calculate order quantities and create order templates for each location. 1. A Flow that ingests the data from each of these order templates into Parabola to create orders through MarketMan's API. These Flows let Juice Press forecast the par level based on sales and compare it with real-time inventory to automatically order the correct amount. As Juice Press saw the efficiency that Parabola created, they began to view it as an essential workflow optimization tool with tons of opportunity for time 
and labor cost savings. They took on the position that anything that was time-consuming, manual, and repetitive should be done in Parabola. ## 400 hours per week in manual work saved At this point, Parabola is saving Juice Press over 400 hours per week in manual work and over $200,000 a year across the whole company. With the tip allocation Flow, HR and Payroll teams are saving a total of 200+ hours per week in manual work, which translates to over $2,000 per week in labor costs for the company. By automating their ordering workflow for each store location, they’re saving 15 hours per week and eliminating the need to hire full-time help just to keep up their manual workflows. By implementing Parabola, the Juice Press team has unlocked a ton of possibilities for increasing the efficiency of workflows across their business. They’re actively working with Parabola to identify new Flows to continue to uncover new and creative ways to save time and money. — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Magic Spoon is building an automation-first culture and saving 500+ hours a year Source: https://parabola.io/customers/magic-spoon ## Scaling retail meant more complexity, not more headcount Magic Spoon launched in 2019 with a bold idea: bring back the joy of childhood cereal, but rebuild it with high protein, no sugar, and clean ingredients. The colorful boxes quickly won fans online. The brand is now everywhere—moving from a DTC-only model to major retailers such as Target, Walmart, Costco, Kroger, and Albertsons. Starting as a DTC-native company and then switching gears to focus on retail brought a new challenge: scaling shifting operations and systems at the same pace as the brand’s growth. With retail expansion came a flood of new complexities: new channels with different requirements, business assumptions that no longer applied, and the need to build new capabilities on a team designed for a DTC playbook. All of this added hidden layers of work to keep operations running smoothly. Each file looked different, each reconciliation took hours, and each mistake risked delaying orders or critical business decisions. Joshua Levy, Director of IT, saw the strain up close. Hiring your way out of the problem only masks the issues and doesn't solve them. “Workflow automation was always something that I was passionate about… I’ve been waiting for the time where these softwares are simple enough for the end users to design,” he said. "The best solutions for problems like these often come from the people doing the day-to-day work—all you have to do is enable and guide them" Magic Spoon needed a way to stay lean while scaling fast. ## Automating key workflows with Parabola Joshua first heard about Parabola through his brother-in-law, who suggested it as a better option than the Zapier and Pipedream automations they were using at the time. Around the same time, Magic Spoon’s head of logistics came across Parabola in a conversation with other operators and encouraged the team to take a closer look. After evaluating the tool, Joshua and the team saw the potential. “Parabola delivered tangible results almost immediately, and that’s why it’s one of our top initiatives for 2026,” he said. The team turned to Parabola to take on their most time-consuming work, with Michael Thorson from the logistics team championing the rollout and building the first key flows. The impact was immediate: - **Month end reconciliations:** What once stretched 10 hours each month now takes just one or two - **Increased team capacity:** What used to be unattainable due to available resources became a recurring flow that brought insights that would have otherwise been missed - **Integration maintenance:** Daily recurring maintenance tasks were automated which saved time, and automated alerts allowed for addition controls to reduce risk These early wins Michael was able to build shifted the company’s perspective: automation wasn’t just a side project, it could become a core part of Magic Spoon's future plan to grow rapidly. ## Making automation a company-wide skill To spread that mindset, Magic Spoon hosted a *Parabola Day*. Every team—operations, finance, HR, and even the leadership team—came together to learn how to build in Parabola. > Fifty percent of the company has already experimented with Parabola. Our goal is that by 2026, automation becomes a natural part of how everyone approaches their work.” This cultural investment also opened the door to experiments with AI. Joshua’s team has been building internal tools to take on repetitive, frustrating tasks like EDI error handling. Instead of waiting on slow support or digging through specs, the system suggests resolutions instantly, pulling from Magic Spoon’s own history of solved tickets. “It’s the perfect use case for AI,” Joshua said. “We can get the recommendation instantly and keep orders moving.” Together, Parabola and AI are giving Magic Spoon the foundation to scale without scaling headcount. ## 500 hours back, and a blueprint for lean growth With Parabola in place, Magic Spoon has unlocked both time savings and better insights: - **~500 hours saved annually:** Time reclaimed across IT, operations and finance workflows - **Higher accuracy:** Joshua estimates at least a 10% improvement in accuracy for month end close reporting - **Real-time alerts:** High-impact orders flagged immediately to keep fulfillment on track - **Faster insights:** Teams get the data they need in minutes instead of hours Most importantly, Magic Spoon can scale without adding headcount at the same pace. The company's vision for 2026 centers on empowering teams to solve their own workflow challenges, with Parabola as a key enabler of that self-sufficiency. > From leadership down, everyone agrees that Parabola is one of our top initiatives for 2026. The question is moving from, ‘Do we need to hire someone?’ to ‘Can we use Parabola first?’” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # Mockingbird saves tens of thousands of dollars—and their supply chain team’s sanity. Source: https://parabola.io/customers/mockingbird ## Scaling operations without an ERP When Parabola landed on the Mockingbird team’s desk, they were managing increasing operational complexity across multiple warehouses—all without the overhead of a traditional ERP system. After launching their first strollers in 2019, the company experienced explosive growth during 2020 when traditional retail channels were disrupted. “We really took off amid Covid when a lot of the retailers shut down. Buyers could no longer go into the stores to trial strollers,” Murphy explains. This had parents looking for an online experience that could rival shopping for strollers in stores. (Mockingbird ships strollers for free and offers customers 30 days to try the product, commitment-free.) Murphy leads Mockingbird’s supply chain operations, overseeing areas from production planning and inventory management to warehouse operations and ocean freight. Working alongside a lean group of cross-functional and external collaborators, her team coordinates a complex logistics network with agility. “Like many startups, we keep things flexible,” Murphy says. “We use Shopify, Anvyl, and we have our WMS integrations. A lot of our planning tools are still in Google Sheets and Excel. It worked for us early on—but as we scaled, the manual overhead grew too.” Before implementing Parabola, Mary spent hours each Monday manually pulling and combining data from SQL queries, warehouse systems, and production schedules across four manufacturers. “It took hours to piece everything together across multiple sheets and data sources,” Murphy recalls. This manual process wasn’t just time-consuming—it also increased the chance of errors and slowed the team’s ability to respond to changing demand. This made inventory allocation particularly challenging. With 70% of customers shipping from Mockingbird’s east coast warehouse, any misallocations to the west coast added $17–20 per stroller in unnecessary costs. “That’s a lot of money,” Murphy notes. “Our products are not small.” ## Turning disparate data into decisions At the heart of Mockingbird’s Parabola Flows is a 250-step “Weeks of Supply” Flow that pulls and blends data from: - Real-time inventory levels from Shipwire - Sales data from Shopify (including complex bundle SKU breakdowns) - Production schedules across manufacturers - Forecast data from Google Sheets - Warehouse performance metrics from Looker This system supports the team’s flexible fulfillment model. Mockingbird purchases strollers and canopies separately to allow for more dynamic inventory decisions. “We don’t come pre-bundled,” Murphy explains. “That gives us more flexibility—but it does add some complexity on the backend.” ### Example of consolidated inventory reporting in Parabola The team also implemented sophisticated warehouse logic through Parabola. “If it can ship in full from the non-optimal warehouse, it will. If it can't, then it splits,” Murphy says. This helps balance service and cost. With Parabola, the supply chain team now has clear visibility into inventory and forecasting metrics by warehouse. This enables more proactive rebalancing and sharper inventory decisions. ## Operational wins that add up Parabola has delivered measurable improvements across the supply chain and beyond: - The team now saves ~6 hours a week on manual data work - Better allocation has saved tens of thousands of dollars in reduced shipping costs - Cross-functional teams can now access key metrics even when Murphy is out - Other teams, including Quality & Marketing, have begun using Parabola for data capture and analysis & forecasting The impact on daily operations has been significant. “Even something as simple as downloading all our container receivings from our WMS and sending them to a Google Sheet…our Parabola Flow saves me 5–10 minutes a day, but it adds up,” Murphy says. The tool has also boosted collaboration across the org. “We had a demand planning meeting this morning,” Murphy shares, “and I asked our marketing manager, ‘Can you add this line of data into your charts?’ And she said, ‘Oh yeah, it’s in Parabola—I’ve got it.’” Murphy estimates the improved allocation process alone has already saved “tens of thousands of dollars…and only growing.” With thousands of orders per week, even small efficiency gains have a major financial impact. The success of the implementation has led to wider adoption. > Our team encouraged other departments to explore Parabola because it’s been so valuable.” Murphy says. “I can actually step away from my desk and know the data’s still flowing and that things are running smoothly.” Looking ahead, the team is continuing to build. They’re currently developing a warehouse SLA Flow to track processing time and throughput against forecasted demand. “The goal is to show how many orders each warehouse is processing daily, how many units they’re shipping, and how that compares to our forecasts,” Murphy explains. [Try vendor scorecard reporting in Parabola to track OTIF delivery rates.](https://parabola.io/use-cases/vendor-scorecard-reporting) Parabola is now embedded across Mockingbird’s operations, providing the scalable data infrastructure the company needs as it expands beyond strollers — without the cost or rigidity of traditional ERP systems. --- # Pair Eyewear saves at least $50k annually by automating their inbound PO process Source: https://parabola.io/customers/pair-eyewear ## As Pair Eyewear expanded their wholesale business, they faced a hurdle: An influx of POs arriving as messy PDFs. Wilner on the data side remembers the early days of their growth all too well. “We were using Narvar as our order management system,” he recalls, and they needed to be able to pull data from that system for internal reporting. But there was a major limitation: They could only export their data as a CSV to an email address. This seemingly small issue created a significant bottleneck in their data pipeline, preventing the team from accessing crucial information about orders in real time. Meanwhile Headapohl on the ops side was grappling with a different set of problems. As his team expanded, so did their need for analytical firepower. “We needed a sandbox,” Headapohl explains, “somewhere we could prototype and iterate without constantly competing for engineering resources.” The operations team found themselves in a constant tug-of-war, trying to balance their growing needs with the limited availability of technical team members. And as Pair expanded their wholesale business, they faced a new hurdle: an influx of purchase orders arriving as messy, hard-to-work-with PDFs. The volume was so high that they were considering hiring a full-time employee just to manage the process of getting that data cleaned and sent to their 3PLs. > We needed a sandbox where we could prototype and iterate without constantly competing for engineering resources. ###### **Zachary Headapohl**CX & Operations Strategy at Pair Eyewear ## Pair Eyewear used Parabola to parse inbound email PDFs — then automate the cleaning, sending, and visualization of that data. When Wilner first discovered Parabola, it was through Narvar, their order management system. They were unable to get the data from that platform into Snowflake, so they were looking for something that could push data from email to S3 (they had a home-grown ingestion system from S3 into Snowflake). But what started as a solution to one specific problem soon blossomed into something that has become a sort of foundational tool for the ops team. Headapohl and his team quickly realized Parabola’s potential beyond simple integrations. It became their sandbox — a place where they could rapidly prototype and build automations to support their processes and data needs without relying on engineering resources. It essentially created an army of citizen developers out of mostly non-technical ops folks. For Headapohl, the full scale of Parabola’s power became apparent as he worked through a hefty project in which he was trying to merge returns data with quality data. “So all of this would be pretty easy to do in Excel if I had a super computer…but I’m dealing with like a half a million records across 36 different fields in some cases, and am standardizing them so I can stack them and then create unique identifiers. To have something to power that is pretty rare,” Headapohl explains. Most recently, they’ve been using Parabola’s PDF parsing capabilities to solve Pair’s wholesale order processing challenge. Typically, Pair receives PDF POs from retailers via email. They had to manually convert them to .csv files so they could 1) send it to the 3PL and place the order for shipment and 2) let their planning team know how much inventory is going out the door so they can track revenue metrics. Because their wholesaler doesn’t have EDI or any electronic transfer capabilities, they were stuck in a very manual process. This led them to scope Parabola’s PDF parsing step (which combines OCR vision technology with AI to both read and contextualize PDFs with extreme accuracy) to see if it could start doing this work for them. Spoiler alert: It could. Here’s how they’ve now automated this whole process — instead of hiring someone to manage it full time: 1. Retailers send POs to a Pair email, which is forwarded into Parabola 1. Parabola scrapes the email for a PDF, then automatically cleans it and puts it into a spreadsheet format 1. From there, it lands in S3, and from S3 they pick it up and bring it into Snowflake > It’s been very, very accurate from day one. I actually barely put any prompts into the AI — I just kind of turned it on, added the columns I wanted, and set it free. It’s a really, really big unlock for us. These PDFs can be like six pages of really poorly formatted order data, that even to a human, can be hard to read. ###### **Zachary Wilner**Leading Data & Analytics at Pair Eyewear ## Now, the whole ops team is enabled with the power to automate their workflows. As Pair Eyewear continues to grow and evolve, Parabola has become an integral part of their technology stack. Even if they wanted to get rid of Parabola, they couldn't, according to Wilner: “We can’t ever rip this out, the ops 
team has become so dependent on it,” he says. But this only seems to improve his working relationship with Headapohl and ops as the person leading data at Pair. Pair’s experience with Parabola started with a simple Flow to solve a critical (but simple) problem they were facing. Now? The whole ops team is enabled with a data tool that gives them the power to automate their workflows, clean and access their data, and iterate on team-wide processes without the support of engineering or data teams. Plus, Pair can report: 1. At least $50k annual savings because they didn’t need 
to bring on full-time resources to manage wholesale PO data management. 1. Dramatically reduced time spent on monthly reporting. As Wilner puts it, "Instead of spending three or four hours twice a month troubleshooting and going back and forth with a data engineer as to why one record broke an entire ingestion, we just run a Flow.” 1. A paper trail that documents internal processes. “You end up with step-by-step instructions on how to run some of the task work, which ultimately reduces the time and effort to accomplish that task work,” Wilner says. And the benefits don’t always have to come from the most complex use cases according to Heapohl: “I think some of the stuff we do with Parabola is really simple, but to do it manually in Excel…I think we’ve all been there where you’re like: What is the one thing that is keeping this from pivoting or merging into the data that I need?” To bring us home, Wilner mentioned someone unfamiliar with Parabola that they brought into the PDF project and immediately threw into the weeds. His takeaway? “This tool rocks.” > Parabola doesn’t only execute on your processes — if you manage your Flows well, it also creates a very distinct space for documentation which is super helpful when we bring new teammates on board. ###### **Zachary Headapohl** CX & Operations Strategy at Pair Eyewear — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Passport cut invoice auditing time by 80% and scaled smarter with Parabola Source: https://parabola.io/customers/passport ## A high-growth business weighed down by manual work Passport helps e-commerce brands go global—from checkout to customs. But behind the scenes, a lot of their success depended on people doing tedious, manual work. As the company scaled and acquired a new business, the operations team faced a growing challenge: how to continue supporting more customers without increasing headcount or slowing down the team. “We’re a very operationally heavy company,” said [Ashlea Holt](https://www.linkedin.com/in/ashlea-holt/), Senior Manager of Business Operations at Passport. “We had amazing internal tech, but we were still relying on people to solve problems that tools could solve. It wasn’t sustainable.” ## A faster, easier way to automate processes without engineering Ashlea’s team started experimenting with Parabola to tackle some of their most time-consuming workflows. Early on, they automated their internal and customer-facing freight tracker, which had been taking 30 to 45 minutes a day to update by hand. “Now it’s ready every morning at 8am. No one has to touch it.” They went on to automate one of their most painful processes: monthly 3PL invoice auditing. The work included aggregating charges, auditing line items against rate cards, and flagging discrepancies for follow-up. It used to take the team 3 to 5 full business days each month. Now the same process takes just one business day, and they’re on track to reduce that even further in the coming weeks. > It used to be a huge lift every month. Now it’s down to a single day, and we’re expecting to cut that to just a few hours. No one misses doing it the old way. Freeing people from the tasks they hate is one of the best morale boosters you can offer.” Parabola also became the go-to solution for high-priority operational issues that popped up unexpectedly. One team had been spending two hours a day manually auditing manifests for a complex, bundled product. The process involved cross-referencing data across days’ worth of shipment trackers and manifests, often spanning more than 1,000 shipments per day. Within days, they built a Parabola flow that flagged only the shipments needing attention—transforming a tedious, error-prone task into one that’s clean, intuitive, and easy to review. That kind of agility has shifted how Passport’s operations team approaches problem-solving. “Every quarter I ask the front line operations team to identify their biggest time-suck, like those hours manually spent digging for exceptions,” said Kat Sun, Chief Operating Officer at Passport. > “Instead of queuing up an engineering ticket, our first move now is, ‘Can Parabola fix this?’ More than half the time, it can.” ## Time saved, headcount preserved, and a smarter way to scale With Parabola in place, the Biz Ops team has not only saved time—they’ve helped shift the entire company’s mindset. Instead of defaulting to hiring or requesting engineering help, teams now ask if Parabola can solve it first. - **3PL invoice auditing reduced from 3–5 days to 1 business day:** Full charge aggregation and review now happens faster, with more time savings expected soon - **Freight tracking process fully automated:** Daily updates that once took up to 45 minutes now happen automatically with no manual work - **Faster response to high-priority issues:** Built new data workflows in days, avoiding weeks of engineering backlog - **On track to 2x customer base within 12 months:** Helped scale an acquired business with only two new hires - **Sharper resourcing decisions:** Teams now think more critically about whether tasks need to be owned by a person or automated with Parabola - **Boosted morale:** Replaced frustrating tasks with automation, freeing people up to focus on higher-impact work “With peak season ahead, we’re building out more use cases to help us scale without drowning in manual work,” Ashlea said. > “It’s not just about saving time. It’s about staying focused on real customer problems instead of constantly playing catch-up.” ‍ --- *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Rhone doubled their operational capacity with Parabola Source: https://parabola.io/customers/rhone ## When growth outpaces headcount In late 2023, Rhone’s operations team faced a perfect storm: With no ability to add headcount and rapidly increasing complexity across their business, the team was struggling to keep up with growing demand. Their VP of Operations Nancy Yellen was watching her team burn out from manual processes that required constant attention, particularly during peak seasons. > We don’t have the leisure of a next man up on this team,” Yellen explained. "We’re not the revenue generators, we’re the back office. So unless we’re coming up with huge saving strategies, we’re not adding bodies.” For Chris Garbutt, Rhone’s Operational Business Analyst, the daily reality of these constraints meant constantly juggling urgent requests while trying to maintain critical business processes. “I act as a liaison to all of the business, so anyone can come to me with a question, and I’ll go find an answer,” he told Parabola. This meant spending hours diving into Excel sheets, reading formulas, and trying to piece together how different departments were getting to their numbers. But the challenge wasn’t just about time—it was also about knowledge transfer. Many critical processes existed only in the heads of one or two people, creating significant business risk. This all came to a head during the 2023 peak season, when the team faced its toughest challenge yet. Orders weren’t flowing properly, integrations were failing, and the process of identifying missing orders required 45 minutes of manual report pulling and reconciliation. The emotional toll on the team was significant, creating friction between operations and enterprise systems teams. The situation was particularly challenging during peak season, when the team needed to perform order tie-outs three times per day. “We were spending hours and hours doing these tie-outs,” Garbutt recalled. “And you were doing that for orders *and* for fulfillment.” The pressure was immense, as failing to catch issues could result in customer backorders and shipping delays. ## Automating with urgency A well-timed email introduced Rhone to Parabola, and Garbutt began building automated workflows to address their most pressing challenges immediately. ### Automated tie-out processes The first priority was creating automated “tie-out” processes for orders and fulfillment. They needed to ensure that orders properly flowed between systems and that shipments were properly tracked. #### How to reconcile inventory across systems with Parabola Before Parabola, these tie-outs required pulling multiple reports from different systems (Shopify, NetSuite, their 3PL), saving them in folders, and performing complex Excel lookups and matches. “Each run would take 35 to 40 minutes,” Garbutt noted. “Mainly because you’ve also got to wait for these reports to fall, they’re not quick.” ### Inventory tracking during 3PL changes Another critical automation came during a major warehouse move, where the team needed to track millions of dollars worth of inventory moving between facilities. “I wanted to know where every single SKU and every single box was,” Garbutt explained, “whether that was on a truck in transit, whether it was sitting on a staging pallet at the front of the building, or whether it was in the other building on a pallet or already been put to a shelf.” This visibility proved crucial when some shipments warranted splitting due to exceeding standard insurance coverage. ### Shipment visibility tracker The team’s automation journey expanded rapidly as they discovered new opportunities: One particularly impactful Flow was built for their inbound logistics specialist, who manages hundreds of shipments from factories into their US distribution center. #### Example of an automated inbound freight tracking Flow in Parabola “He was an email machine,” Yellen said. “He was rebuilding his workload every day, compiling in-transit updates coming in through email in another email to the team for visibility.” The team created a Flow that automatically updates their database with shipment dates, countries of origin, and HTS codes, saving 5-6 hours of manual updates per week. ### Plus Flows to support EDI compliance and finance reconciliation for month-end closing And for their finance team, the automation of reconciliation processes has been transformative. “Finance’s Flows are related to credit memo and refund records out of Shopify compared to NetSuite,” Garbutt told Parabola. These automated reconciliations have helped the finance team significantly reduce their month-end close time, saving approximately 20 hours per month. The team’s latest project focuses on streamlining their planning department’s allocation process. “It’s something that they spend up to 7 hours a week on,” Garbutt notes. “And there’s human error that comes with this kind of work...a comma in the wrong spot can throw off your entire math.” The new Flow will not only save time but also reduce the risk of costly mistakes. ## From on-call stress to smooth sailing The implementation of Parabola has transformed how Rhone’s operations team functions. What was once a process requiring team members to be on-call nights and weekends during peak season has been reduced to simply monitoring Slack notifications: “Now when someone’s on call, all they have to do is just look at Slack every hour and make sure there aren’t excessive alerts,” Yellen explains. The impact has been particularly dramatic in their logistics operations. The team handled a doubling of shipments and a tripling of active SKUs without adding headcount. Their finance team has found substantial efficiencies within the month-end process, allowing the team to reclaim upwards of 7 hours per week. Time that was previously spent on manual data entry and reconciliation is now being redeployed in more value-adding areas and analysis. Perhaps most importantly, the automation has de-personalized system issues and improved cross-team collaboration. “It changed the whole paradigm of our ability to be insightful and transparent without emotion,” Yellen notes. “Now there’s just a report, and it says what it says.” Looking ahead, Rhone continues to expand their use of Parabola, with Garbutt building new Flows to support their growing complexity. Parabola has become their solution for scaling operations without adding headcount, providing what Yellen calls their “saving grace” in an environment of increasing business complexity and constrained resources. > Any little sliver of promise that we can get from Parabola is a godsend,” Yellen concludes. “And that’s how we were sold on the cost for this year.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Ruggable cut KPI reporting time by 65% with Parabola Source: https://parabola.io/customers/ruggable ## Spreadsheets couldn’t keep up with growth Ruggable is known for its machine-washable rugs, but behind the product is a global supply chain moving raw materials through the U.S., Canada, the U.K., Australia. Ramida Hanratanakool manages inbound freight and supports key parts of the supply planning process at Ruggable. As the business expanded, so did the complexity of her work: more partners, more data, more pressure to plan accurately. “We were expanding our partner base and the data inputs exploded. We were basically in Google Sheets and Excel,” Ramida said. “We didn’t have a scalable pipeline. And it would have been extremely labor intensive to build one internally.” Her team was spending hours each week pulling shipment data from freight forwarders and ocean carriers, manually cleaning and combining it, and formatting it into reports. Forecasting models required full-day refreshes. And with no single source of truth, it was hard to explain inconsistencies across different teams. “We’d get questions like, ‘Why are there so many versions of this data?’” she said. “We knew what was most accurate, but it wasn’t always obvious to others.” ## From manual reporting to a trusted, automated system Ramida turned to Parabola to build an automated foundation for operations and planning. With support from Parabola’s solutions team, she created two critical workflows: one for inbound freight and one for supply planning. The inbound workflow pulls data from freight and trucking partners, standardizes it, adds key context like pallet counts and projected costs, and exports it into three structured tables: shipments, containers, and SKU-level data. > We use it to calculate everything from weights and volumes to warehouse capacity. It’s become our source of truth.” The supply planning workflow takes marketing and manufacturing forecasts and converts them into raw material purchasing plans—what to order, when, and how much. It also stores historical snapshots to help with variance analysis and reconciliation. “These are flows I wouldn’t have been able to build on my own,” Ramida said. “We knew the outcome we wanted, and Parabola helped us get there.” ## Hours saved, decisions made faster, and fewer dependencies With Parabola running in the background, Ramida’s team has shifted from reactive reporting to proactive planning. Instead of spending hours cleaning spreadsheets or tracking down discrepancies, they now focus on the insights that drive real operational impact. - **Cut KPI reporting time by 65%:** Time spent on weekly reporting dropped from 90 minutes to just 30. - **Forecasting time dropped from 8 hours to 2:** The team can now refresh forecasts more frequently, unlocking better agility. - **4 to 5 hours of analyst time reclaimed each week:** A teammate who once managed reporting end-to-end now focuses on higher-impact projects. - **More confident, data-informed decisions:** A single source of truth means less time validating data and more time acting on it. - **Less reliance on engineering:** The team handles API connections and data transformations without waiting on technical resources. One of the biggest improvements? Understanding *why* forecasts change. Previously, the team would dig through Slack threads and old files to find the reason. Now, they use Parabola to automatically compare forecast versions and surface the drivers behind every shift. > It’s saved us so much time and stress. We finally have the visibility we need to explain what’s happening.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Screenverse cut monthly data processing from weeks to minutes with Parabola Source: https://parabola.io/customers/screenverse ## When manual data work couldn't keep up with growth [Screenverse](https://www.screenversemedia.com/) manages one of the largest digital screen networks in the physical world—over 120,000 screens across digital billboards, screens in gas stations and EV charging sites, office and residential buildings, restaurants and bars, and 30+ venue types. Their programmatic network delivers billions of impressions, connecting brands to consumers at the moments that matter most. But behind those billions of impressions was a data team drowning in manual work. Karen Brand, Senior QA Data Analyst, and Junko Yoshimaru, Director of Data and Tech Ops, were managing 120,000+ screens with 96 columns of data for every individual screen across 20+ media partners. Their biggest media network alone operates 60,000 screens across the United States, Canada, and Puerto Rico. For Karen, the workload had become unsustainable. "I have to audit their inventory monthly, and it takes me 3 weeks to finish an audit, so by the time I actually finish an audit, I'm about to start a new one." The team had grown from managing everything in Google Sheets to trying various data validation tools, but nothing could handle their complex workflows and massive data volumes. As Screenverse scaled from 12 to over 45 employees, the data bottleneck was becoming critical. "We can't just hire one person for every 5 new partners when the company's goal is to expand and onboard new media networks throughout the year." Junko explains. ## AI-powered automation transforms complex data workflows When Karen discovered Parabola, the team thought they were looking for a simple data validation tool. They found something much more powerful. "All we wanted was a data validation tool. And within the first week, we were like, oh my god, oh my god, oh my god. Every week, it was something new." The breakthrough came with Parabola's AI-powered data extraction. One partner sent address data in wildly inconsistent formats—sometimes "street, city, state, zip," other times "zip, state, city, street," and occasionally just emailed addresses instead of physical addresses. > We like to use the extract with AI step that allows us to automatically identify what looks like a zip code, what looks like a street address.Whereas before, Junko and I would have to text to columns, hope for the best, and then try to manually clean up from there. For 120,000 screens, that takes a long time.” The scale becomes clear in their most complex workflow: transforming data from one row per screen to 168 rows per screen, which for a 20,000-screen media partner creates over 3.1 million rows. "Excel can't open that," Junko notes. "Or if you do, it deletes the data." Now, Parabola handles what used to be a manual nightmare: automatic lookups that translate location data for different advertising platforms simultaneously, automated auditing workflows with 70+ transformation steps, and error management that filters bad data and sends reports directly to account managers via Slack. ## Hours saved, scaling unlocked, and strategic focus restored The transformation has been dramatic. Screenverse's data team went from reactive manual processing to proactive strategic planning. Instead of spending weeks cleaning data and managing Excel crashes, they focus on insights that drive real business impact. - **Cut audit time from 3 weeks to 12 seconds:** Complex partner audits that previously consumed entire months now complete almost instantaneously. - **Eliminated Excel bottlenecks:** Processing 3+ million rows of data no longer crashes systems or requires splitting files into dozens of pieces. - **Freed up strategic capacity:** Team members can now focus on higher-impact projects instead of manual data cleanup and validation. - **Automated error management:** Bad data is automatically identified, filtered out, and reported to account managers without manual intervention. - **Enabled seamless collaboration:** Team members can run each other's workflows during PTO through shared Slack integrations, eliminating single points of failure. - **Replaced static reports with interactive dashboards:** Sales teams can now drill down into performance metrics themselves rather than waiting for custom analysis. The efficiency gains have fundamentally changed how the team operates. "Parabola basically gave us all role responsibility promotions, where we got to do so many cool other things, because we got that time back," Karen reflects. Most importantly for a rapidly growing company, the solution scales with their business. Instead of hiring additional staff for every new media partner, they can handle growth through automation. > When we first started Parabola, all we wanted was a data validation tool. Every week, it was something new that now we're doing things on Parabola that we did not know were possible last month.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Seed scaled their operations and saved 500+ hours a year with Parabola Source: https://parabola.io/customers/seed-health ## Manual reporting couldn’t keep up with growth [Seed Health](https://seed.com/) is a microbiome science company pioneering clinically validated innovations for gut and whole-body health. When DeJuan Hall joined as Senior Supply Chain Analyst, he was stepping into a company on a steep growth curve. Seed had nearly doubled headcount and was shipping hundreds of thousands of orders a month across D2C subscriptions, Amazon, and retail partners. For DeJuan, the challenge was immediate: his lean operations team was drowning in manual work. > Every morning, I used to spend two hours just verifying reports, pulling them from different platforms, and cleaning up data. That was before I even got to the work of analyzing or sharing insights.” Critical processes like invoice audits were eating up hours. A single vendor file could stretch 40,000 to 60,000 lines, and reconciling it was done line by line in spreadsheets. “We needed a way to standardize reporting and reduce the repetitive work,” DeJuan said. “Otherwise, we’d spend more time cleaning data than actually running the business.” ## From spreadsheets to scale: automating workflows with Parabola Seed’s SVP of Operations introduced DeJuan to Parabola. With more than 15 years of experience in operations, he immediately saw its potential. “I look at Parabola as a ground roots tool,” DeJuan said. “It takes out all the report pulls, integrations, and cleaning of data and puts it into one workflow where we can centralize everything and output to our dashboards.” The team focused first on their most time-consuming workflows: - **Invoice audits:** What once took two hours on invoice day now takes five to ten minutes. Parabola cleans raw invoice data, runs rate calculations, and flags discrepancies instantly. - **Daily reporting:** Reports now run on a schedule and flow into Tableau already standardized. Instead of jumping between platforms, DeJuan checks one screen. - **Inventory management:** Min-max reports track stock levels at 3PL partners so the team can act before issues escalate. - **Ad hoc requests:** Common asks from other departments are automated, so insights can be delivered in minutes instead of hours. > Anything new that gets added on, like new data channels, APIs, or integrations, we can scale with it. That’s the awesome thing about Parabola.” ## 500+ hours reclaimed, agility gained With Parabola powering his workflows, DeJuan and the Seed operations team have shifted from repetitive manual work to higher-value projects. Instead of spending hours pulling reports or auditing invoice lines, they can focus on building tools, spotting trends, and supporting company growth with confidence. The impact shows up in both day-to-day work and long-term scalability: - **500+ hours reclaimed annually:** Up to 2 hours saved each day to focus on higher-value projects - I**nvoice audits in minutes:** Down from hours, with mismatches flagged automatically - **Thousands per month saved:** Savings from not purchasing a transportation management system - **Faster reporting:** Daily and ad hoc reports delivered in minutes, not hours - **Trusted data foundation:** Tableau dashboards run on outputs that are already clean and consistent “Instead of adding up 40,000 lines in Excel, Parabola tells me at a glance if the invoice matches,” DeJuan said. “If it doesn’t, I can go back to the vendor right away knowing our numbers are right.” Most importantly, Seed gained the agility to scale confidently. > Parabola saves us time, gives us flexibility, and makes us quicker at retrieving data and supplying insights. When you’re building a company that’s doubling in size, that time is invaluable.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How ShipBob cut manual work by 50% and brought more clarity to close with Parabola Source: https://parabola.io/customers/shipbob ## Too much manual work, not enough time for analysis ShipBob is a fast-growing logistics and fulfillment company with a lean finance team supporting operations across the US, UK, Canada, Spain, and Australia. For Brian Johnston, Senior Accountant, month-end close meant juggling thousands of rows of data across multiple spreadsheets while under pressure to hit tight deadlines and answer stakeholder questions quickly. “Every month we’d roll forward Excel files, copy-paste data, rebuild mappings. It was repetitive and very time consuming,” Brian said. “Even small changes like a few new employees meant updating formulas, adding lines in the middle of spreadsheets, and tying numbers back out to confirm completeness.” Labor accruals were especially painful. Invoices came as 20+ page PDFs—often inconsistently formatted or with overlapping periods. Extracting line items by hand, reconciling them against timecard data, and validating numbers against their GL wasn’t just tedious. It made it hard to have the time to do meaningful analysis ask the right questions before in a timely manner. ## Creating consistent, auditable processes at scale ShipBob initially explored Parabola to support shipping cost workflows, one of their most complex and closely monitored areas. During that process, the finance team saw an opportunity to automate accruals and reconciliations with Parabola and quickly got started. They began with a proof of concept focused on labor accruals. After that Brian built flows for bonus accruals that pulled in HR data, employee rosters, and mapping files. > “Now I just drop in four files, and it maps everything automatically. I used to spend hours redoing the same vlookups every month. Now it just works.” That success expanded to other workflows, including stock-based compensation, cash postings and other accruals across multiple entities. Brian even uses Parabola to generate journal entries he can upload straight into their accounting software. Instead of redoing manual work each month, the logic now lives in repeatable flows. “It’s faster, more accurate, and more enjoyable.” ## Time saved, fewer errors, and better conversations Parabola has helped ShipBob’s finance team move faster, answer questions with confidence, and free up time for higher-impact work. - **50%+ reduction in prep time:** Automating repeatable workflows means no more rebuilding spreadsheets from scratch. - **Time shifted from manual prep to real analysis:** Instead of scrambling before close, the team now walks into meetings with insights and a clear explanation of what changed and why. - **More strategic conversations with stakeholders:** With less time spent on manual prep, the team can now dig into the data and ask sharper, more informed questions. - **More consistent, error-free reporting:** Standardized flows eliminate small formula mistakes that used to slip through. - **Fewer dependencies and less headcount pressure:** Parabola helps the lean team stay efficient without needing to add more people. The result is a smoother, faster, and more confident close process. > “It’s not just about saving time—it’s about using that time to do better work. Parabola takes away the boring, low-value work so we can focus on what actually matters.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # How Tecovas uses Parabola to make sure their best sellers are never out of stock Source: https://parabola.io/customers/tecovas ## Drowning in manual data work Before Parabola, Tecovas’s teams were drowning in manual data work. **The ops team’s critical daily snapshot of supply chain and distribution metrics was housed in what their VP of Supply Chain called “an Excel/Google Sheets/Dropbox/.csv patched-together mixture of documents.”** Referred to as their “heartbeat report,” it required ops team members to constantly manually compile data from multiple sources. The data was only as current as the previous day’s 4 AM update, meaning teams were constantly working with stale information. “The team would pull that data, they would merge it against NetSuite data, and then they would merge it against what was stored in Dropbox, which housed our forecasting data,“ explains Peterson. This manual process made it nearly impossible to make timely decisions about inventory and warehouse staffing. Meanwhile, the planning and sourcing teams were facing their own challenges. **For Apparel and Accessories Planner Chelsey Parker, there is a huge focus on what they are internally calling their “never-out-of-stock” initiative.** “We have core SKUs, but we haven’t really ever done an analysis on the missed opportunity that we’ve had by not having those SKUs in stock at all times,” explains Parker. Their Excel-based system was so cumbersome that “it was virtually impossible to work in it, especially for footwear, where there’s like 40 sizes per SKU.“ Her team was on the line though to keep the most popular styles in stock at all times—but they also want to be cautious about overstocking. With rows and rows of difficult to understand data, this was a huge challenge. **And for the sourcing team and Senior Category Manager Mallory Smith, there were endless data messes to clean up.** Smith describes how vendors would send weekly shipping reports with inconsistent formatting—some using incorrect vendor codes, others adding random spaces in PO numbers. With 40,000 units on a single PO and month-long shipping windows, tracking actual deliveries was a nightmare. “None of that is tracked systematically,” Smith explains. “So a lot of the data that my team is parsing through on a weekly basis, we’re pushing through Parabola to help us analyze it instead of spending most of our time building it.” ## Turning data into action ### Automated heartbeat reporting **This report has led to a six-figure reduction in labor costs, lowering Tecovas’s CPU and contributing to more efficient 3PL operations, especially during their peak season.** Tecovas built an automated flow in Parabola that pulls data hourly from multiple sources to give teams real-time visibility into supply chain performance. The flow combines historical data with current sales, orders, and shipping information from their 3PL provider Geodis. “The goal of the team is to not attempt to wake up each day and try to find the problems. The goal is to wake up and be *alerted* to the problems and go in attack mode,” explains Peterson. This real-time visibility is especially crucial during their Black Friday/Cyber Monday period, which drives a significant portion of annual sales. One thing this automated reporting enables is quick decisions about labor planning: “If we saw a variation to planned sales or we saw us missing forecasts through about the 12 o’clock hour, we could then say, hey guys, lets reallocate resources accordingly, come back tomorrow and save four or five hours of overhead,“ Peterson explains. This flexibility with their 3PL partner helps optimize labor costs while maintaining service levels. > There has also been a tangible impact on both Tecovas’ CPU (cost-per-unit) and UPH (units-per-hour) metrics: “We measure what we’re doing on a weekly basis from a CPU and a UPH perspective. We’re always looking for a decreased CPU or an increased UPH—and that’s 100% where we’re driving the business.” And overall, this “heartbeat reporting” gives the ops team major leverage throughout the business: “If I’m on our leadership team and I get a question at two in the morning and I want to know how we’re doing from a distribution and supply chain perspective, I should be able to get that data in real time and be able to self-serve and answer that question,” Peterson says. That’s Tecovas’s reality—and this self-serve, data-enabled company culture is the outcome of getting full-team buy-in on a tool like Parabola. ### Never-out-of-stock management **By keeping their most popular SKUs always in stock, the Tecovas team identified hundreds of thousands in revenue opportunity—just in jeans. ** The planning team created a streamlined system that transformed their inventory management capabilities. “Reducing those data dumps into one really simple paste, and then enabling us to simplify the formulas into quicker moving formulas, means that all our planners can actually work in these sheets efficiently,” says Parker. ### Explore consolidated inventory reporting in Parabola This new visibility revealed significant opportunities. In apparel alone, Parker identified “hundreds of thousands in opportunity *just* in jeans, because we weren't looking at them month over month.” The Annie boot, their number one women’s SKU, was frequently out of stock in at least one color. The team now uses average per store per day (APS) calculations to quantify missed sales opportunities and better predict inventory needs. The impact has been so dramatic that core SKUs are now “probably 80% of the focus of the planning team,” according to Parker. “Those new SKUs are a much smaller percentage of our time.” ### Vendor data automation **Access to clean, current vendor data gives the Tecovas team more bargaining power when it comes to negotiating with vendors.** For the sourcing team, Parabola automatically cleans and standardizes vendor data, maintaining a historical record while providing clean, current information. “It basically keeps a history of everything that they’ve ever sent us, and then adds the latest file that they sent us. So I have a clean upload,” explains Smith. This automation has improved vendor relationships and accountability. “We’re having a conversation with one of our biggest footwear factories this week around their data,” Smith shares. “Being able to look at that data side by side and be like, ‘Hey, this is how inaccurate you are on a weekly basis,’ has allowed us to go to them and say, ‘These are the improvements that we need.’” The automated system also feeds multiple downstream reports, including a CX Incoming Inventory Report that helps customer service representatives answer product availability questions and enables the wholesale team to better manage client expectations. ## Impact you can measure If it hasn’t already been abundantly clear the ways that Tecovas has benefited from their work in Parabola, they report: - Six-figure savings in relation to labor management - Hundreds of thousands of dollars of additional revenue from ensuring popular SKU availability - Decreased CPU and increased UPH - 20 hours a month freed up for the team’s data analyst to actually analyze data — not just corral and clean - Self-serve data access for CX, marketing, and the C-Suite - Real-time data visibility that leads to better decision making across the board And those are just the more tangible benefits. The impact has been so significant that when asked what would happen if Parabola disappeared tomorrow, Parker responds: “I would have to completely redo all of our flow sheets…I just don't think it would be manageable.” The sourcing team “would be drowning in fixing data,” according to Smith, with impacts rippling across the organization. > As for Peterson: “To be able to recreate what we’re doing in Parabola on a day-to-day basis, without the tool, to be quite frank, it would require human capital. We would have to add resources to achieve the same results. We would have to bring people in to run these reports, to crunch the data, to aggregate it and provide it in maybe a close-to-the-same (but probably not even the same) format. As Tecovas continues its rapid expansion, Parabola has become essential infrastructure for managing their growing complexity. “Parabola has been the tool that has been able to get data out of our systems and support that initiative,” concludes Peterson. --- # Uber Freight keeps tabs on thousands of shipments with Parabola Source: https://parabola.io/customers/uber-freight ## Managing hundreds of unique shipping processes manually Uber Freight works with hundreds of customers, many of which have unique processes for how to manage updates and tracking throughout the shipping process. It’s important that Uber Freight can let their customers know where loads are at any given time. To accomplish this, the team was working in pods. Each pod was responsible for tracking their customers’ shipments, reaching out to carriers when they were missing information in the transportation management system (TMS), and inputting it into systems so they could send out updates according to each customer’s standard operating procedure (or SOP). It was an extremely manual process, and there wasn’t a streamlined way to handle it. Alastair Streitz, Senior Manager at Uber Freight, was in search of a fix for the two big challenges he was facing in this model: 1) a lack of information from carriers and 2) juggling dozens of unique processes. “How do we, in a more centralized and programmatic way, pull the data out of the TMS, and instead of having 30 people all with their own slightly different excel macros and their different SOPs sending out check call requests, push those out through one system that’s configured to make sure we’re still taking a customer-centric approach to getting shipping updates?” Streitz wondered. This is where Parabola came in. ## A smarter way to manage shipment data Streitz set up Parabola to run the same process that Uber Freight was doing manually: At its simplest form, it’s asking the carrier to either push check call updates through the TMS or send over the information so the team can input it manually. But getting that tedious, manual job off of the pods’ plates took building a really thoughtful Parabola Flow. Here’s how it works: Parabola scrapes a purpose-built Uber Freight database that houses tracking-related load data, and ingests it into a Parabola table to see where there are SLA breaches. From there: Parabola dedupes the information to ensure that there are no duplicate requests to carriers. Since carriers receive a huge number of emails, making sure that they’re as focused and streamlined as possible can improve carrier response rates. From there, a series of Parabola steps would compare that data against a Google Sheet with carrier contact information to determine who check call requests needed to be sent to. Up next, the data is broken up by load into the pods that are responsible for updating the shipment information, and Parabola formats emails that will automatically be sent on their behalf detailing delinquent shipments to the responsible carriers. Parabola calls the Front API, where those emails are then sent from. That whole process, from figuring out which shipments are missing updates, to cleaning the data and formatting it into digestible tables, and even sending the emails from the pods in charge of the shipments, happens without any human intervention. This has been a game changer for Uber Freight. ## Improving compliance and operational efficiency The results of implementing this automated track and trace process were both quick and impactful. The Uber Freight team realized: - An up-and-to-the-right improvement in check call compliance — the key unit of success for the team running track and trace - Cost savings as a result of operational streamlining - A central SOP with built-in configurability to meet customer expectations - Ability to scale the operation seamlessly as business grows “As soon as we launched the first component of the Parabola automation,” Streitz began, “we saw an increase in our top-line compliance figure, even in just a week.” As a reminder: That’s that check call metric, or the number that indicates how consistently Uber Freight is getting updates from their carriers on time. > We’ve seen sustained levels of high check call compliance, it’s almost like every week we hit a new all time best,” Streitz said. In terms of overall operational improvements, the bandwidth the Uber Freight team was using to cater to numerous SOPs (and all of their unique data) for hundreds of customers has been freed up due to implementing a tried-and-tested process that works for serving most of their customers. The freed-up bandwidth previously spent on routine, administratively heavy, and transactional tasks is now refocused to the most critical, service-intensive tracking exceptions to deliver a better experience to their customers. Uber Freight is passionate about providing a white glove experience — and this allows their customers to get better, faster insights into their shipments and leads to the kind of experience they’re known for providing. #### Working with Parabola I could beat around the bush and summarize what Streitz said when I asked him about his experience implementing this Flow and onboarding Parabola in general, but I’ll just let him speak for himself here: “It was extremely smooth, especially considering we were working through a very complex, arduous use case. I was incredibly impressed with just how quickly the team would pick up exactly what we were trying to accomplish and build out either a sample of how we might accomplish it or just build it themselves.” > I felt very, very confident that the Parabola team was building to my use case and that whenever we found something that needed to be tweaked, we could take it to them and they would answer any questions. 10/10 onboarding experience.” — *This customer story is for informational purposes only. Parabola makes no warranties, express or implied, in this document.* --- # Accounts receivable aging Source: https://parabola.io/use-cases/accounts-receivable-aging-report --- # Accrual calculations Source: https://parabola.io/use-cases/accrual-calculations --- # Address parsing & enrichment Source: https://parabola.io/use-cases/address-parsing-enrichment --- # Asset depreciation management Source: https://parabola.io/use-cases/asset-depreciation-management --- # Accounts payable Source: https://parabola.io/use-cases/accounts-payable-automation --- # Ad spend & ROAS reporting Source: https://parabola.io/use-cases/ad-spend-roas-reporting --- # Clear-to-build summaries Source: https://parabola.io/use-cases/clear-to-build-summary --- # COA standardization Source: https://parabola.io/use-cases/coa-standardization --- # COGS analysis Source: https://parabola.io/use-cases/cogs-analysis --- # Commission & payout calculations Source: https://parabola.io/use-cases/commission-payout-calculations --- # Days on hand reporting Source: https://parabola.io/use-cases/days-on-hand-reporting --- # Demand forecasting Source: https://parabola.io/use-cases/demand-forecasting --- # Inbound container tracking Source: https://parabola.io/use-cases/inbound-container-tracking --- # Labor cost reporting Source: https://parabola.io/use-cases/labor-reporting --- # Multi-warehouse inventory consolidation Source: https://parabola.io/use-cases/multi-warehouse-consolidation --- # Pipeline & CRM reporting Source: https://parabola.io/use-cases/pipeline-crm-reporting --- # Promotional & pricing analysis Source: https://parabola.io/use-cases/promotional-pricing-analysis --- # Retailer sell-through reporting Source: https://parabola.io/use-cases/retailer-sell-through-reporting --- # Sales & revenue reporting Source: https://parabola.io/use-cases/sales-revenue-reporting --- # Variance analysis Source: https://parabola.io/use-cases/variance-analysis --- # Carrier scorecard reporting Source: https://parabola.io/use-cases/carrier-scorecard-reporting --- # Three-way PO matching Source: https://parabola.io/use-cases/three-way-po-match --- # Track average days to return delivery Source: https://parabola.io/use-cases/average-days-to-return-delivery --- # Cash flow forecasting Source: https://parabola.io/use-cases/cash-flow-forecasting --- # Cash reconciliation Source: https://parabola.io/use-cases/cash-reconciliation --- # Digitize CIPLs & validate them against your POs Source: https://parabola.io/use-cases/cipl-digitization-validation --- # Journal entry creation Source: https://parabola.io/use-cases/create-journal-entries --- # Customs document digitization Source: https://parabola.io/use-cases/customs-document-digitization --- # Deferred revenue reconciliation Source: https://parabola.io/use-cases/deferred-revenue-reconciliation --- # Standardize country names into ISO codes Source: https://parabola.io/use-cases/iso-country-code-standardization --- # Extract email body & attachment data Source: https://parabola.io/use-cases/extract-email-body-attachment-data --- # Pull shipment details from notification emails Source: https://parabola.io/use-cases/extract-shipment-details-email-bodies --- # Flexport inventory reconciliation Source: https://parabola.io/use-cases/flexport-inventory-reconciliation --- # Parcel invoice auditing Source: https://parabola.io/use-cases/parcel-invoice-audit --- # Freight invoice digitization Source: https://parabola.io/use-cases/freight-invoice-digitization --- # Turn booking emails into clean order records Source: https://parabola.io/use-cases/freight-order-entry --- # GL mapping Source: https://parabola.io/use-cases/gl-mapping --- # Classify every product against HTS in minutes Source: https://parabola.io/use-cases/hts-code-classification --- # Inventory allocation Source: https://parabola.io/use-cases/inventory-allocation --- # Inventory reconciliation Source: https://parabola.io/use-cases/inventory-reconciliation --- # Inventory replenishment monitoring Source: https://parabola.io/use-cases/inventory-replenishment-monitoring --- # Invoice parsing & line-item categorization Source: https://parabola.io/use-cases/invoice-parsing-line-item-categorization --- # Landed cost calculation Source: https://parabola.io/use-cases/landed-cost --- # Marketplace delivery date audit Source: https://parabola.io/use-cases/marketplace-delivery-date-audit --- # Month-end close Source: https://parabola.io/use-cases/month-end-close --- # Sales channel consolidation Source: https://parabola.io/use-cases/order-consolidation --- # Order fulfillment exception alerting Source: https://parabola.io/use-cases/order-fulfillment-alerting --- # Analyze order issue rates across support & fulfillment Source: https://parabola.io/use-cases/order-issue-rate-analysis --- # Order routing Source: https://parabola.io/use-cases/order-routing --- # Parcel spend forecasting Source: https://parabola.io/use-cases/parcel-spend-forecasting --- # Property tax processing Source: https://parabola.io/use-cases/property-tax-ticketing --- # Purchase order tracking Source: https://parabola.io/use-cases/purchase-order-tracking --- # Returns management reporting Source: https://parabola.io/use-cases/returns-management --- # SKU standardization & mapping Source: https://parabola.io/use-cases/sku-standardization-mapping --- # Spend classification Source: https://parabola.io/use-cases/spend-classification --- # Tariff scenario modeling Source: https://parabola.io/use-cases/tariff-scenario-modeling --- # Text drivers as soon as the milestone fires Source: https://parabola.io/use-cases/text-drivers-track-trace --- # Auto-answer track-and-trace emails Source: https://parabola.io/use-cases/track-and-trace-email-automation --- # Track production orders against sourcing reality Source: https://parabola.io/use-cases/production-sourcing-reporting --- # Vendor chargebacks reporting Source: https://parabola.io/use-cases/vendor-chargebacks --- # Vendor price list import Source: https://parabola.io/use-cases/vendor-price-list-import --- # Vendor scorecard reporting Source: https://parabola.io/use-cases/vendor-scorecard-reporting --- # Wholesale OTIF scorecards Source: https://parabola.io/use-cases/wholesale-otif-scorecard --- # Parse & categorize Zendesk tickets with AI Source: https://parabola.io/use-cases/zendesk-ticket-parsing-categorization --- # The #1 no-code alternative to Alteryx Source: https://parabola.io/parabola-vs/alteryx --- # The #1 no-code alternative to Boomi Source: https://parabola.io/parabola-vs/boomi --- # The Google Sheets alternative for more nuanced workflows Source: https://parabola.io/parabola-vs/google-sheets --- # The #1 no-code alternative to Make Source: https://parabola.io/parabola-vs/make --- # The Excel alternative for more nuanced workflows Source: https://parabola.io/parabola-vs/microsoft-excel --- # The #1 no-code alternative to n8n Source: https://parabola.io/parabola-vs/n8n --- # The #1 no-code alternative to Power Automate Source: https://parabola.io/parabola-vs/power-automate --- # The #1 no-code alternative to Power BI Source: https://parabola.io/parabola-vs/power-bi --- # Prepare the data you visualize in Tableau Source: https://parabola.io/parabola-vs/tableau --- # The #1 no-code alternative to Tray.ai Source: https://parabola.io/parabola-vs/tray-ai --- # The #1 no-code alternative to Workato Source: https://parabola.io/parabola-vs/workato --- # The #1 Zapier alternative for complex workflows Source: https://parabola.io/parabola-vs/zapier --- # 3PL Source: https://parabola.io/glossary/3pl --- # 3PL invoice audit Source: https://parabola.io/glossary/3pl-invoice-audit --- # 3PL management Source: https://parabola.io/glossary/3pl-management --- # 3PL scorecarding Source: https://parabola.io/glossary/3pl-scorecarding --- # 3PL WMS Source: https://parabola.io/glossary/3pl-wms --- # Accounts payable automation Source: https://parabola.io/glossary/accounts-payable-automation --- # Accounts payable automation process Source: https://parabola.io/glossary/accounts-payable-automation-process --- # Accounts payable automation technology Source: https://parabola.io/glossary/accounts-payable-automation-technology --- # Accounts payable invoice automation Source: https://parabola.io/glossary/accounts-payable-invoice-automation --- # Accounts payable reconciliation Source: https://parabola.io/glossary/accounts-payable-reconciliation --- # Accounts receivable aging report Source: https://parabola.io/glossary/accounts-receivable-aging-report --- # Accounts receivable reconciliation Source: https://parabola.io/glossary/accounts-receivable-reconciliation --- # Accrual calculations Source: https://parabola.io/glossary/accrual-calculations --- # Address parsing & enrichment Source: https://parabola.io/glossary/address-parsing-enrichment --- # Advanced shipping notice Source: https://parabola.io/glossary/advanced-shipping-notice --- # AI workflow automation Source: https://parabola.io/glossary/ai-workflow-automation --- # Analytics automation Source: https://parabola.io/glossary/analytics-automation --- # API Source: https://parabola.io/glossary/api --- # Asset depreciation management Source: https://parabola.io/glossary/asset-depreciation-management --- # Audit automation Source: https://parabola.io/glossary/audit-automation --- # Automated data extraction Source: https://parabola.io/glossary/automated-data-extraction --- # Automated data reporting Source: https://parabola.io/glossary/automated-data-reporting --- # Automated supply and demand platform Source: https://parabola.io/glossary/automated-supply-and-demand-platform --- # Back office automation Source: https://parabola.io/glossary/back-office-automation --- # Back office automation software Source: https://parabola.io/glossary/back-office-automation-software --- # Backorder reporting Source: https://parabola.io/glossary/backorder-reporting --- # Bill of lading Source: https://parabola.io/glossary/bill-of-lading --- # Bill of lading digitization Source: https://parabola.io/glossary/bill-of-lading-digitization --- # Bill of material consolidation Source: https://parabola.io/glossary/bill-of-material-consolidation --- # Bill of materials Source: https://parabola.io/glossary/bill-of-materials --- # BOMs & COGs variance reporting Source: https://parabola.io/glossary/boms-cogs-variance-reporting --- # Bookkeeping automation Source: https://parabola.io/glossary/bookkeeping-automation --- # Budget vs. actual reporting Source: https://parabola.io/glossary/budget-vs-actual-reporting --- # Bundled SKU component reporting Source: https://parabola.io/glossary/bundled-sku-component-reporting --- # Business automation services Source: https://parabola.io/glossary/business-automation-services --- # CAC Source: https://parabola.io/glossary/cac --- # Carrier Source: https://parabola.io/glossary/carrier --- # Carrier management Source: https://parabola.io/glossary/carrier-management --- # Carrier scorecarding Source: https://parabola.io/glossary/carrier-scorecarding --- # Cartonization optimization Source: https://parabola.io/glossary/cartonization-optimization --- # Cash flow forecasting Source: https://parabola.io/glossary/cash-flow-forecasting --- # Cash reconciliation Source: https://parabola.io/glossary/cash-reconciliation --- # Certificate of origin Source: https://parabola.io/glossary/certificate-of-origin --- # Chargebacks Source: https://parabola.io/glossary/chargebacks --- # Cohort analysis Source: https://parabola.io/glossary/cohort-analysis --- # Commercial invoice Source: https://parabola.io/glossary/commercial-invoice --- # Commercial invoice digitization Source: https://parabola.io/glossary/commercial-invoice-digitization --- # Consolidated inventory reporting Source: https://parabola.io/glossary/consolidated-inventory-reporting --- # Container optimization analysis Source: https://parabola.io/glossary/container-optimization-analysis --- # Container utilization modeling Source: https://parabola.io/glossary/container-utilization-modeling --- # Contract manufacturers Source: https://parabola.io/glossary/contract-manufacturers --- # CSV Source: https://parabola.io/glossary/csv --- # Customs broker Source: https://parabola.io/glossary/customs-broker --- # Customs clearance Source: https://parabola.io/glossary/customs-clearance --- # Customs document digitization Source: https://parabola.io/glossary/customs-document-digitization --- # Cycle count Source: https://parabola.io/glossary/cycle-count --- # Cycle stock Source: https://parabola.io/glossary/cycle-stock --- # Data analytics automation Source: https://parabola.io/glossary/data-analytics-automation --- # Data automation Source: https://parabola.io/glossary/data-automation --- # Data automation platform Source: https://parabola.io/glossary/data-automation-platform --- # Data enriching Source: https://parabola.io/glossary/data-enriching --- # Data mapping Source: https://parabola.io/glossary/data-mapping --- # Data munging Source: https://parabola.io/glossary/data-munging --- # Data normalization Source: https://parabola.io/glossary/data-normalization --- # Data preparation Source: https://parabola.io/glossary/data-preparation --- # Data preparation platform Source: https://parabola.io/glossary/data-preparation-platform --- # Data process automation Source: https://parabola.io/glossary/data-process-automation --- # Data transformation Source: https://parabola.io/glossary/data-transformation --- # Data warehouse automation Source: https://parabola.io/glossary/data-warehouse-automation --- # Demand forecasting Source: https://parabola.io/glossary/demand-forecasting --- # Demand planning Source: https://parabola.io/glossary/demand-planning --- # Demurrage Source: https://parabola.io/glossary/demurrage --- # Demurrage charge mitigation Source: https://parabola.io/glossary/demurrage-charge-mitigation --- # Detention Source: https://parabola.io/glossary/detention --- # Digital supply chain management Source: https://parabola.io/glossary/digital-supply-chain-management --- # Digital supply chain transformation Source: https://parabola.io/glossary/digital-supply-chain-transformation --- # Dimensional weight Source: https://parabola.io/glossary/dimensional-weight --- # Direct procurement Source: https://parabola.io/glossary/direct-procurement --- # Direct-to-consumer Source: https://parabola.io/glossary/direct-to-consumer --- # Document digitzation Source: https://parabola.io/glossary/document-digitzation --- # Drayage Source: https://parabola.io/glossary/drayage --- # Drop shipping Source: https://parabola.io/glossary/drop-shipping --- # Duty drawback Source: https://parabola.io/glossary/duty-drawback --- # Duty drawback reporting Source: https://parabola.io/glossary/duty-drawback-reporting --- # Duty drawback validation & discrepancy management Source: https://parabola.io/glossary/duty-drawback-validation-discrepancy-management --- # EDI Source: https://parabola.io/glossary/edi --- # Email parsing Source: https://parabola.io/glossary/email-parsing --- # Email parsing platform Source: https://parabola.io/glossary/email-parsing-platform --- # Employee onboarding automation Source: https://parabola.io/glossary/employee-onboarding-automation --- # Enterprise resource planning Source: https://parabola.io/glossary/enterprise-resource-planning --- # Enterprise resource planning automation platform Source: https://parabola.io/glossary/enterprise-resource-planning-automation-platform --- # ERP automation Source: https://parabola.io/glossary/erp-automation --- # ETL Source: https://parabola.io/glossary/etl --- # Excel automation Source: https://parabola.io/glossary/excel-automation --- # Excel automation platform Source: https://parabola.io/glossary/excel-automation-platform --- # Excel spreadsheet automation Source: https://parabola.io/glossary/excel-spreadsheet-automation --- # Final mile delivery Source: https://parabola.io/glossary/final-mile-delivery --- # Freight & logistics Source: https://parabola.io/glossary/freight-logistics --- # Freight & parcel audits Source: https://parabola.io/glossary/freight-parcel-audits --- # Freight & parcel invoice audit Source: https://parabola.io/glossary/freight-parcel-invoice-audit --- # Freight audit Source: https://parabola.io/glossary/freight-audit --- # Freight broker Source: https://parabola.io/glossary/freight-broker --- # Freight forwarder Source: https://parabola.io/glossary/freight-forwarder --- # Freight invoice digitization Source: https://parabola.io/glossary/freight-invoice-digitization --- # Freight order entry Source: https://parabola.io/glossary/freight-order-entry --- # Freight quote request email parsing Source: https://parabola.io/glossary/freight-quote-request-email-parsing --- # Freight reconiliation Source: https://parabola.io/glossary/freight-reconiliation --- # Freight reconiliation automation Source: https://parabola.io/glossary/freight-reconiliation-automation --- # Fulfillment Source: https://parabola.io/glossary/fulfillment --- # Fulfillment automation Source: https://parabola.io/glossary/fulfillment-automation --- # Fulfillment automation platform Source: https://parabola.io/glossary/fulfillment-automation-platform --- # Full truckload Source: https://parabola.io/glossary/full-truckload --- # General ledger mapping Source: https://parabola.io/glossary/general-ledger-mapping --- # Harmonized tariff code Source: https://parabola.io/glossary/harmonized-tariff-code --- # HS code Source: https://parabola.io/glossary/hs-code --- # HTS Codes Source: https://parabola.io/glossary/hts-codes --- # Inbound container arrival alerting Source: https://parabola.io/glossary/inbound-container-arrival-alerting --- # Inbound inventory Source: https://parabola.io/glossary/inbound-inventory --- # Indirect procurement Source: https://parabola.io/glossary/indirect-procurement --- # Intermodal transportation Source: https://parabola.io/glossary/intermodal-transportation --- # Inventory accuracy Source: https://parabola.io/glossary/inventory-accuracy --- # Inventory aging report Source: https://parabola.io/glossary/inventory-aging-report --- # Inventory allocation Source: https://parabola.io/glossary/inventory-allocation --- # Inventory burndown reporting Source: https://parabola.io/glossary/inventory-burndown-reporting --- # Inventory control Source: https://parabola.io/glossary/inventory-control --- # Inventory forecasting Source: https://parabola.io/glossary/inventory-forecasting --- # Inventory planning Source: https://parabola.io/glossary/inventory-planning --- # Inventory planning platform Source: https://parabola.io/glossary/inventory-planning-platform --- # Inventory replenishment monitoring Source: https://parabola.io/glossary/inventory-replenishment-monitoring --- # Inventory turnover Source: https://parabola.io/glossary/inventory-turnover --- # Inventory turnover analysis Source: https://parabola.io/glossary/inventory-turnover-analysis --- # Inventory turnover ratio Source: https://parabola.io/glossary/inventory-turnover-ratio --- # Inventory visibility Source: https://parabola.io/glossary/inventory-visibility --- # Inventory visibility platform Source: https://parabola.io/glossary/inventory-visibility-platform --- # Invoice aging reports Source: https://parabola.io/glossary/invoice-aging-reports --- # Invoice generation Source: https://parabola.io/glossary/invoice-generation --- # Invoice parsing & line item categorization Source: https://parabola.io/glossary/invoice-parsing-line-item-categorization --- # Invoice processing Source: https://parabola.io/glossary/invoice-processing --- # Invoice processing automation Source: https://parabola.io/glossary/invoice-processing-automation --- # Invoice processing platform Source: https://parabola.io/glossary/invoice-processing-platform --- # Kitting Source: https://parabola.io/glossary/kitting --- # Landed cost Source: https://parabola.io/glossary/landed-cost --- # Landed cost calculation Source: https://parabola.io/glossary/landed-cost-calculation --- # Last-mile delivery Source: https://parabola.io/glossary/last-mile-delivery --- # Lead management automation Source: https://parabola.io/glossary/lead-management-automation --- # Lead time Source: https://parabola.io/glossary/lead-time --- # LTL Source: https://parabola.io/glossary/ltl --- # Months on hand reporting Source: https://parabola.io/glossary/months-on-hand-reporting --- # OCR automation Source: https://parabola.io/glossary/ocr-automation --- # OCR platform Source: https://parabola.io/glossary/ocr-platform --- # Omnichannel Source: https://parabola.io/glossary/omnichannel --- # Omnichannel fulfillment Source: https://parabola.io/glossary/omnichannel-fulfillment --- # On time in full Source: https://parabola.io/glossary/on-time-in-full --- # Onboarding automation Source: https://parabola.io/glossary/onboarding-automation --- # Optical character recognition Source: https://parabola.io/glossary/optical-character-recognition --- # Optical character recognition automation Source: https://parabola.io/glossary/optical-character-recognition-automation --- # Optical character recognition platform Source: https://parabola.io/glossary/optical-character-recognition-platform --- # Order consolidation Source: https://parabola.io/glossary/order-consolidation --- # Order entry Source: https://parabola.io/glossary/order-entry --- # Order entry automation Source: https://parabola.io/glossary/order-entry-automation --- # Order entry platform Source: https://parabola.io/glossary/order-entry-platform --- # Order entry workflow Source: https://parabola.io/glossary/order-entry-workflow --- # Order fulfillment Source: https://parabola.io/glossary/order-fulfillment --- # Order fulfillment alerting Source: https://parabola.io/glossary/order-fulfillment-alerting --- # Order issue rate analysis Source: https://parabola.io/glossary/order-issue-rate-analysis --- # Order management Source: https://parabola.io/glossary/order-management --- # Order management automation Source: https://parabola.io/glossary/order-management-automation --- # Order management platform Source: https://parabola.io/glossary/order-management-platform --- # Order reconciliation Source: https://parabola.io/glossary/order-reconciliation --- # Order routing Source: https://parabola.io/glossary/order-routing --- # OTIF Source: https://parabola.io/glossary/otif --- # Outbound inventory Source: https://parabola.io/glossary/outbound-inventory --- # Overstocking Source: https://parabola.io/glossary/overstocking --- # Packing list Source: https://parabola.io/glossary/packing-list --- # Packing list digitization Source: https://parabola.io/glossary/packing-list-digitization --- # Parcel spend forecasting Source: https://parabola.io/glossary/parcel-spend-forecasting --- # Parcel spend management Source: https://parabola.io/glossary/parcel-spend-management --- # Parcel zones Source: https://parabola.io/glossary/parcel-zones --- # PDF Source: https://parabola.io/glossary/pdf --- # PDF parser Source: https://parabola.io/glossary/pdf-parser --- # PDF parsing Source: https://parabola.io/glossary/pdf-parsing --- # PDF parsing platforms Source: https://parabola.io/glossary/pdf-parsing-platforms --- # Pick and pack Source: https://parabola.io/glossary/pick-and-pack --- # Procurement Source: https://parabola.io/glossary/procurement --- # Product handling Source: https://parabola.io/glossary/product-handling --- # Product listing optimization Source: https://parabola.io/glossary/product-listing-optimization --- # Product usage reporting Source: https://parabola.io/glossary/product-usage-reporting --- # Proof of delivery Source: https://parabola.io/glossary/proof-of-delivery --- # Protective tariff Source: https://parabola.io/glossary/protective-tariff --- # Purchase order Source: https://parabola.io/glossary/purchase-order --- # Purchase order tracking Source: https://parabola.io/glossary/purchase-order-tracking --- # Rate sheet Source: https://parabola.io/glossary/rate-sheet --- # Replenishment Source: https://parabola.io/glossary/replenishment --- # Reporting automation Source: https://parabola.io/glossary/reporting-automation --- # Returns management Source: https://parabola.io/glossary/returns-management --- # Returns processing Source: https://parabola.io/glossary/returns-processing --- # Revenue leakage Source: https://parabola.io/glossary/revenue-leakage --- # Revenue leakage automation Source: https://parabola.io/glossary/revenue-leakage-automation --- # Revenue reconciliation Source: https://parabola.io/glossary/revenue-reconciliation --- # Reverse logistics Source: https://parabola.io/glossary/reverse-logistics --- # ROAS Source: https://parabola.io/glossary/roas --- # ROAS & CAC reporting Source: https://parabola.io/glossary/roas-cac-reporting --- # Route optimization Source: https://parabola.io/glossary/route-optimization --- # Safety stock Source: https://parabola.io/glossary/safety-stock --- # Sales commission reporting Source: https://parabola.io/glossary/sales-commission-reporting --- # Sales order Source: https://parabola.io/glossary/sales-order --- # Sell-through rate Source: https://parabola.io/glossary/sell-through-rate --- # Shipment tracking Source: https://parabola.io/glossary/shipment-tracking --- # Shipping & visibility Source: https://parabola.io/glossary/shipping-visibility --- # Shipping audits Source: https://parabola.io/glossary/shipping-audits --- # Shipping costs Source: https://parabola.io/glossary/shipping-costs --- # SKU Source: https://parabola.io/glossary/sku --- # SKU standardization & mapping Source: https://parabola.io/glossary/sku-standardization-mapping --- # Sourcing Source: https://parabola.io/glossary/sourcing --- # Spend classification Source: https://parabola.io/glossary/spend-classification --- # Spend queue management Source: https://parabola.io/glossary/spend-queue-management --- # Stockout Source: https://parabola.io/glossary/stockout --- # Supplier relationships Source: https://parabola.io/glossary/supplier-relationships --- # Supplier scorecarding Source: https://parabola.io/glossary/supplier-scorecarding --- # Supplier spend analysis across contract manufacturers Source: https://parabola.io/glossary/supplier-spend-analysis-across-contract-manufacturers --- # Suppliers Source: https://parabola.io/glossary/suppliers --- # Supply and demand forecasting Source: https://parabola.io/glossary/supply-and-demand-forecasting --- # Supply chain digital transformation Source: https://parabola.io/glossary/supply-chain-digital-transformation --- # Supply chain disruptions Source: https://parabola.io/glossary/supply-chain-disruptions --- # Supply chain management Source: https://parabola.io/glossary/supply-chain-management --- # Supply chain risks Source: https://parabola.io/glossary/supply-chain-risks --- # Tariff war Source: https://parabola.io/glossary/tariff-war --- # Three-way PO match reconciliation Source: https://parabola.io/glossary/three-way-po-match-reconciliation --- # Timesheet automation Source: https://parabola.io/glossary/timesheet-automation --- # Tip allocation Source: https://parabola.io/glossary/tip-allocation --- # TMS Source: https://parabola.io/glossary/tms --- # TMS automation Source: https://parabola.io/glossary/tms-automation --- # TMS platform Source: https://parabola.io/glossary/tms-platform --- # Track & trace update requests Source: https://parabola.io/glossary/track-trace-update-requests --- # Track and trace Source: https://parabola.io/glossary/track-and-trace --- # Track and trace automation Source: https://parabola.io/glossary/track-and-trace-automation --- # Track and trace platform Source: https://parabola.io/glossary/track-and-trace-platform --- # Tracking in-transit inventory Source: https://parabola.io/glossary/tracking-in-transit-inventory --- # Tracking inbound freight Source: https://parabola.io/glossary/tracking-inbound-freight --- # Tracking inventory levels Source: https://parabola.io/glossary/tracking-inventory-levels --- # Transportation Source: https://parabola.io/glossary/transportation --- # Vendor chargeback reporting Source: https://parabola.io/glossary/vendor-chargeback-reporting --- # Vendor compliance & vendor chargeback analysis Source: https://parabola.io/glossary/vendor-compliance-vendor-chargeback-analysis --- # Vendor management Source: https://parabola.io/glossary/vendor-management --- # Vendor relationships Source: https://parabola.io/glossary/vendor-relationships --- # Vendor scorecards Source: https://parabola.io/glossary/vendor-scorecards --- # Warehousing Source: https://parabola.io/glossary/warehousing --- # What is Inventory Management? Source: https://parabola.io/glossary/inventory-management --- # What is Inventory Reconciliation? Source: https://parabola.io/glossary/inventory-reconciliation --- # White-glove delivery Source: https://parabola.io/glossary/white-glove-delivery --- # Wholesale otif fulfillment scorecarding Source: https://parabola.io/glossary/wholesale-otif-fulfillment-scorecarding --- # WMS Source: https://parabola.io/glossary/wms --- # WMS automation Source: https://parabola.io/glossary/wms-automation --- # WMS automation platform Source: https://parabola.io/glossary/wms-automation-platform --- # Workflow automation Source: https://parabola.io/glossary/workflow-automation --- # Accrual accounting software Source: https://parabola.io/processes/accrual-accounting-software Learn about accrual accounting and explore the best accrual accounting software options to improve your business efficiency and drive better results. --- # Data Reconciliation for Ops and Finance Teams Source: https://parabola.io/processes/data-reconciliation-automation Learn what data reconciliation is, why manual processes break down at scale, and how ops and finance teams automate reconciliation across inventory, invoices, cash, and reporting. --- # Document Digitization for Ops and Finance Teams Source: https://parabola.io/processes/automating-document-digitization Learn how ops and finance teams automate document digitization to extract structured data from emails, invoices, packing lists, and customs documents without manual entry. --- # Order and Inventory Management for Ops and Finance Teams Source: https://parabola.io/processes/automating-order-and-inventory-management Learn how ops and finance teams automate order and inventory management to improve visibility, prevent stock issues, and scale operations without adding manual work. --- # Budget vs. actual reporting software Source: https://parabola.io/processes/budget-vs-actual-reporting-software Compare top budget vs. actual reporting software solutions. Learn how modern tools streamline variance analysis and find the best platform for your needs. --- # Cash reconciliation software Source: https://parabola.io/processes/cash-reconciliation-software Discover the best cash reconciliation software for your business. Compare leading solutions and learn how to automate transaction matching with Parabola. --- # Best ETL Softwares: A Comparison Guide Source: https://parabola.io/processes/best-etl-softwares-a-comparison-guide Compare top ETL tools. Discover how modern data integration tools streamline operations and find the best platform for your business needs. --- # Freight audit software Source: https://parabola.io/processes/best-freight-audit-software A comprehensive guide to freight audit software: what it does, why it matters, top tools, best practices, and where automation fits in. --- # Best Excel Alternatives: A Complete Guide Source: https://parabola.io/processes/best-google-sheets-alternatives Learn how ETL automation drives business efficiency and reduces manual data work. Compare top solutions and discover how to streamline data integration with Parabola. --- # Best Landed Cost Calculation Softwares: A Complete Guide Source: https://parabola.io/processes/landed-cost-calculation-software A comprehensive guide to landed cost calculation software: what it is, why it matters, top tools, and best practices for protecting your margins. --- # Best Excel Alternatives: A Complete Guide Source: https://parabola.io/processes/best-excel-alternatives Learn how ETL automation drives business efficiency and reduces manual data work. Compare top solutions and discover how to streamline data integration with Parabola. --- # Parcel spend management software Source: https://parabola.io/processes/parcel-spend-management-software Learn about the best parcel spend management software currently in-market, including how to maximize shipping cost recovery with Parabola. --- # Purchase order management software Source: https://parabola.io/processes/purchase-order-management-software Explore the best softwares for purchase order management, including Parabola's AI-powered workflow automation platform. --- # Track and trace software Source: https://parabola.io/processes/track-and-trace-software Compare top track and trace software solutions. Learn how modern tools improve shipment visibility and find the best platform for your needs. --- # Variance analysis software Source: https://parabola.io/processes/best-variance-analysis-tools-a-comparison-guide A complete guide to variance analysis tools: what they are, why they matter, leading solutions, best practices, and pitfalls to avoid. --- # Vendor compliance management software Source: https://parabola.io/processes/vendor-compliance-management-software Compare top vendor compliance software solutions. Learn how modern tools streamline supplier monitoring and find the best platform for your needs. --- # Vendor scorecard reporting software Source: https://parabola.io/processes/vendor-scorecard-reporting-software A comprehensive guide to vendor scorecarding software: what it is, why it matters, leading tools, and best practices for implementation. --- # Carrier scorecard reporting software Source: https://parabola.io/processes/carrier-scorecard-reporting-software Learn about carrier scorecard reporting and explore the best carrier scorecard reporting software options to improve your business efficiency and drive better results. --- # Returns management software Source: https://parabola.io/processes/returns-management-software Compare leading returns management software, from Loop and Narvar to ReverseLogix and Happy Returns. Learn how Parabola automates returns to cut costs and boost efficiency. --- # Freight invoice digitization software Source: https://parabola.io/processes/freight-invoice-digitization-software Learn about freight invoice digitization and explore the best freight invoice digitization software options to improve your business efficiency and drive better results. --- # Freight order entry software Source: https://parabola.io/processes/freight-order-entry-software Learn about freight order entry and explore the best freight order entry software options to improve your business efficiency and drive better results. --- # Freight tracking software Source: https://parabola.io/processes/freight-tracking-software Learn about freight tracking and explore the best freight tracking software options to improve your business efficiency and drive better results. --- # How to Extract Data from PDFs: A Complete Guide Source: https://parabola.io/processes/how-to-extract-data-from-pdfs-a-complete-guide Learn how to extract data from PDFs using OCR, ML, and traditional parsers. Plus, discover how Parabola's AI-powered solution makes PDF data extraction intuitive and automated. --- # Inventory allocation software Source: https://parabola.io/processes/inventory-allocation-software Learn about inventory allocation and explore the best inventory allocation software options to improve your business efficiency and drive better results. --- # Inventory forecasting software Source: https://parabola.io/processes/inventory-forecasting-software Learn about inventory forecasting and explore the best inventory forecasting software options to improve your business efficiency and drive better results. --- # Inventory management software Source: https://parabola.io/processes/inventory-management-software Learn about inventory management and explore the best inventory management software options to improve your business efficiency and drive better results. --- # Inventory reconciliation software Source: https://parabola.io/processes/inventory-reconciliation-software Learn about inventory reconciliation and explore the best inventory reconciliation software options to improve your business efficiency and drive better results. --- # Order fulfillment software Source: https://parabola.io/processes/order-fulfillment-software Learn about order fulfillment and explore the best order fulfillment software options to improve your business efficiency and drive better results. --- # Order management software Source: https://parabola.io/processes/order-management-software Learn about order management and explore the best order management software options to improve your business efficiency and drive better results. --- # The Complete Guide to Purchase Order Tracking Source: https://parabola.io/processes/the-complete-guide-to-purchase-order-tracking Learn how purchase order tracking automation cuts costs and saves time. Compare top solutions and discover how to optimize your PO management with Parabola. --- # What is budget vs. actual reporting? Source: https://parabola.io/processes/what-is-budget-vs-actual-reporting Learn how to optimize financial performance through budget vs. actual reporting. Compare methods and discover how Parabola's automation capabilities cut variance analysis time by 85%. --- # The Complete Guide to Cash Reconciliation Source: https://parabola.io/processes/what-is-cash-reconciliation Learn how automated cash reconciliation reduces errors and saves time. Discover how modern tools like Parabola can cut reconciliation time by 75% while improving accuracy. --- # The Complete Guide to ETL Source: https://parabola.io/processes/the-complete-guide-to-etl Learn how ETL automation drives business efficiency and reduces manual data work. Compare top solutions and discover how to streamline data integration with Parabola. --- # The Complete Guide to Freight Audits Source: https://parabola.io/processes/what-are-freight-audits Discover how freight audit automation cuts shipping costs and prevents overbilling. Learn best practices and see how Parabola can streamline the audit process. --- # The Complete Guide to GL Mapping Source: https://parabola.io/processes/the-complete-guide-to-gl-mapping Learn how GL mapping automation cuts costs and saves time. Compare top solutions and discover how to optimize your accounting process with Parabola. --- # The Complete Guide to Inventory Reconciliation Source: https://parabola.io/processes/what-is-inventory-reconciliation Learn how inventory reconciliation automation cuts costs and saves time. Compare top solutions and discover how to optimize your stock accuracy with Parabola. --- # The Complete Guide to Landed Cost Calculation Source: https://parabola.io/processes/what-is-landed-cost earn how landed cost calculation automation cuts costs and saves time. Compare top solutions and discover how to optimize your import costs with Parabola. --- # The Complete Guide to Order Consolidation Source: https://parabola.io/processes/the-complete-guide-to-order-consolidation Learn how order consolidation automation cuts costs and saves time. Compare top solutions and discover how to optimize your shipping efficiency with Parabola. --- # The Complete Guide to Order Routing Source: https://parabola.io/processes/the-complete-guide-to-order-routing Learn how order routing automation cuts costs and saves time. Compare top solutions and discover how to optimize your fulfillment process with Parabola. --- # What is parcel spend management? Source: https://parabola.io/processes/what-is-parcel-spend-management Learn about parcel spend management and see how you can perform parcel spend management to improve your business efficiency and drive better results. --- # The Complete Guide to Returns Management Source: https://parabola.io/processes/what-is-returns-management Learn how returns management automation cuts costs and saves time. Compare top solutions and discover how to optimize your returns process with Parabola. --- # The Complete Guide to Spend Classification Source: https://parabola.io/processes/the-complete-guide-to-spend-classification Learn how spend classification automation cuts costs and saves time. Compare top solutions and discover how to optimize your expense tracking with Parabola. --- # The Complete Guide to Track & Trace Source: https://parabola.io/processes/what-is-track-and-trace Learn how track and trace automation cuts costs and saves time. Compare top solutions and discover how to optimize your shipment visibility with Parabola. --- # The Complete Guide to Vendor Scorecard Reporting Source: https://parabola.io/processes/what-is-vendor-scorecard-reporting Learn how vendor scorecard automation cuts costs and saves time. Compare top solutions and discover how to optimize your supplier performance tracking. --- # What is accrual accounting? Source: https://parabola.io/processes/what-is-accrual-accounting Learn about accrual accounting and see how you can perform accrual accounting to improve your business efficiency and drive better results. --- # What is carrier scorecard reporting? Source: https://parabola.io/processes/what-is-carrier-scorecard-reporting Learn about carrier scorecard reporting and see how you can perform carrier scorecard reporting to improve your business efficiency and drive better results. --- # What is freight invoice digitization? Source: https://parabola.io/processes/what-is-freight-invoice-digitization Learn about freight invoice digitization and see how you can perform freight invoice digitization to improve your business efficiency and drive better results. --- # What is freight order entry? Source: https://parabola.io/processes/what-is-freight-order-entry Learn about freight order entry and see how you can perform freight order entry to improve your business efficiency and drive better results. --- # What is freight tracking? Source: https://parabola.io/processes/what-is-freight-tracking Learn about freight tracking and see how you can perform freight tracking to improve your business efficiency and drive better results. --- # What is inventory allocation? Source: https://parabola.io/processes/what-is-inventory-allocation Learn about inventory allocation and see how you can perform inventory allocation to improve your business efficiency and drive better results. --- # What is inventory forecasting? Source: https://parabola.io/processes/what-is-inventory-forecasting Learn about inventory forecasting and see how you can perform inventory forecasting to improve your business efficiency and drive better results. --- # What is inventory management in supply chain? Source: https://parabola.io/processes/what-is-inventory-management-in-supply-chain Learn about inventory management and see how you can perform inventory management to improve your business efficiency and drive better results. --- # What is order fulfillment? Source: https://parabola.io/processes/what-is-order-fulfillment Learn about order fulfillment and see how you can perform order fulfillment to improve your business efficiency and drive better results. --- # What is order management? Source: https://parabola.io/processes/what-is-order-management Learn about order management and see how you can perform order management to improve your business efficiency and drive better results. --- # What is purchase order management? Source: https://parabola.io/processes/what-is-purchase-order-management Learn about purchase order management and see how you can perform purchase order management to improve your business efficiency and drive better results. --- # What is vendor compliance management? Source: https://parabola.io/processes/what-is-vendor-compliance-management Learn about vendor compliance management and see how you can perform vendor compliance management to improve your business efficiency and drive better results. --- # Automate ABM Funnel Stages in Salesforce Source: https://parabola.io/tool/automate-abm-funnel-stages-in-salesforce Automatically update ABM funnel stages in Salesforce as accounts progress from cold to closed using live engagement data. --- # Automatically Analyze Email Subject Line Performance Using Outreach Data Source: https://parabola.io/tool/automatically-analyze-email-subject-line-performance-using-outreach-data Identify your best email subject lines by analyzing Outreach performance data—automate tracking and insights with Parabola. --- # Automatically Sync Event Registrants from Google Sheets to Salesforce Source: https://parabola.io/tool/automatically-sync-event-registrants-from-google-sheets-to-salesforce Automatically create contacts and update campaign statuses in Salesforce using registrant data from Google Sheets. --- # Build an Automated Website Conversion Dashboard Source: https://parabola.io/tool/build-an-automated-website-conversion-dashboard --- # How to combine Amazon Seller Central data with API data Source: https://parabola.io/tool/how-to-combine-amazon-seller-central-data-with-api-data Combine Amazon Seller Central data with API data without writing a single line of code. --- # How to combine Amazon Seller Central data with email data Source: https://parabola.io/tool/how-to-combine-amazon-seller-central-data-with-email-data Combine Amazon Seller Central data with Email data without writing a single line of code. --- # How to combine Amazon Seller Central data with Excel data Source: https://parabola.io/tool/how-to-combine-amazon-seller-central-data-with-excel-data Upload your Excel doc, connect to Amazon Seller Central, and use this ready workflow to merge both datasets — no coding required. --- # How to combine Amazon Seller Central data with Google Sheets data Source: https://parabola.io/tool/how-to-combine-amazon-seller-central-data-with-google-sheets-data Upload your Google Sheets doc, connect to Amazon Seller Central, and use this ready workflow to merge both datasets — no coding required. --- # How to combine Amazon Seller Central data with PDF data Source: https://parabola.io/tool/how-to-combine-amazon-seller-central-data-with-pdf-file-data Upload your PDF file, connect to Amazon Seller Central, and use this ready workflow to merge both datasets — no coding required. --- # How to combine and join tables from your Amazon Seller Central data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-amazon-seller-central-data Combine and join tables from your Amazon Seller Central data without writing a single line of code. --- # How to combine and join tables from your CSV data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-csv-data Combine and join tables from your CSV data without writing a single line of code. --- # How to combine and join tables from your Excel data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-excel-data Combine and join tables from your Excel data without writing a single line of code. --- # How to combine and join tables from your Google Sheets data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-google-sheets-data Combine and join tables from your Google Sheets data without writing a single line of code. --- # How to combine and join tables from your Netsuite data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-netsuite-data Combine and join tables from your Netsuite data without writing a single line of code. --- # How to combine and join tables from your PDF data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-pdf-data Combine and join tables from your PDF data without writing a single line of code. --- # How to combine and join tables from your Shopify data Source: https://parabola.io/tool/how-to-combine-and-join-tables-from-your-shopify-data Combine and join tables from your Shopify data without writing a single line of code. --- # Combine BigQuery and Amazon Seller Central data using AI Source: https://parabola.io/tool/combine-bigquery-and-amazon-seller-central-data-using-ai Automatically combine Amazon Seller Central and BigQuery data without writing a single line of code. --- # Combine BigQuery and Shopify data using AI Source: https://parabola.io/tool/combine-bigquery-and-shopify-data-using-ai Automatically combine Shopify and BigQuery data without writing a single line of code. --- # How to combine CSV data with email data Source: https://parabola.io/tool/how-to-combine-csv-data-with-email-data Combine CSV data with Email data without writing a single line of code. --- # How to combine Excel data with API data Source: https://parabola.io/tool/how-to-combine-excel-data-with-api-data Combine Excel data with API data without writing a single line of code. --- # How to combine Excel data with email data Source: https://parabola.io/tool/how-to-combine-excel-data-with-email-data Combine Excel data with Email data without writing a single line of code. --- # How to combine Excel data with Google Sheets data Source: https://parabola.io/tool/how-to-combine-excel-data-with-google-sheets-data Combine Excel data with Google Sheets data without writing a single line of code. --- # How to combine Google Sheets data with API data Source: https://parabola.io/tool/how-to-combine-google-sheets-data-with-api-data Combine Google Sheets data with API data without writing a single line of code. --- # How to combine Google Sheets data with email data Source: https://parabola.io/tool/how-to-combine-google-sheets-data-with-email-data Combine Google Sheets data with Email data without writing a single line of code. --- # How to combine NetSuite data with Amazon Seller Central data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-amazon-seller-central-data Automatically combine your NetSuite and Amazon Seller Central datasets. Upload and sync sales, inventory, and finance metrics in one place. --- # How to combine NetSuite data with API data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-api-data Combine Netsuite data with API data without writing a single line of code. --- # How to combine NetSuite and BigCommerce data using AI Source: https://parabola.io/tool/combine-netsuite-and-bigcommerce-data-using-ai Automatically integrate NetSuite and Magento data without writing a single line of code. --- # How to combine NetSuite data with Excel data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-excel-data Upload your Excel file, connect to NetSuite, and use this ready workflow to merge both datasets — no coding required. --- # How to combine NetSuite data with Google Sheets data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-google-sheets-data Upload your Google Sheets doc, connect to Shopify, and use this ready workflow to merge both datasets — no coding required. --- # How to integrate NetSuite and HubSpot data using AI Source: https://parabola.io/tool/how-to-integrate-netsuite-and-hubspot-data-using-ai Automatically integrate NetSuite and HubSpot data without writing a single line of code. --- # How to integrate NetSuite and Magento data using AI Source: https://parabola.io/tool/how-to-integrate-netsuite-and-magento-data-using-ai Automatically integrate NetSuite and Magento data without writing a single line of code. --- # How to combine NetSuite data with PDF file data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-pdf-file-data Upload your PDF file, connect to NetSuite, and use this ready workflow to merge both datasets — no coding required. --- # How to integrate NetSuite and Snowflake data using AI Source: https://parabola.io/tool/how-to-integrate-netsuite-and-sharepoint-data-using-ai Automatically integrate NetSuite and SharePoint data without writing a single line of code. --- # How to combine NetSuite data with ShipHero data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-shiphero-data Combine Netsuite data with ShipHero data without writing a single line of code. --- # How to combine NetSuite data with Shopify data Source: https://parabola.io/tool/how-to-combine-netsuite-data-with-shopify-data Combine Netsuite data with Shopify data without writing a single line of code. --- # How to combine NetSuite and Snowflake data using AI Source: https://parabola.io/tool/how-to-combine-netsuite-and-snowflake-data-using-ai Automatically integrate NetSuite and Snowflake data without writing a single line of code. --- # How to integrate NetSuite and Stripe data using AI Source: https://parabola.io/tool/how-to-integrate-netsuite-and-stripe-data-using-ai Automatically integrate NetSuite and Stripe data without writing a single line of code. --- # How to combine PDF data with API data Source: https://parabola.io/tool/how-to-combine-pdf-data-with-api-data Combine PDF data with API data without writing a single line of code. --- # How to combine PDF data with email data Source: https://parabola.io/tool/how-to-combine-pdf-data-with-email-data Combine PDF data with Email data without writing a single line of code. --- # How to combine PDF data with Excel data Source: https://parabola.io/tool/how-to-combine-pdf-data-with-excel-data Combine PDF data with Excel data without writing a single line of code. --- # How to combine PDF data with Google Sheets data Source: https://parabola.io/tool/how-to-combine-pdf-data-with-google-sheets-data Combine PDF data with Google Sheets data without writing a single line of code. --- # How to combine Salesforce and HubSpot data using AI Source: https://parabola.io/tool/how-to-combine-salesforce-and-hubspot-data Automatically integrate Salesforce and HubSpot data without writing a single line of code. --- # How to integrate Salesforce and Mailchimp data using AI Source: https://parabola.io/tool/how-to-integrate-salesforce-and-mailchimp-data-using-ai Automatically integrate Salesforce and Mailchimp data without writing a single line of code. --- # How to integrate Salesforce and NetSuite data using AI Source: https://parabola.io/tool/how-to-integrate-salesforce-and-netsuite-data-using-ai Automatically integrate Salesforce and NetSuite data without writing a single line of code. --- # How to combine Salesforce and Snowflake data using AI Source: https://parabola.io/tool/combine-salesforce-and-snowflake-data-using-ai Automatically integrate Salesforce and Snowflake data without writing a single line of code. --- # How to combine ShipHero data with Amazon Seller Central data Source: https://parabola.io/tool/how-to-combine-shiphero-data-with-amazon-seller-central-data Combine ShipHero data with Amazon Seller Central data without writing a single line of code. --- # How to combine ShipHero data with API data Source: https://parabola.io/tool/how-to-combine-shiphero-data-with-api-data Combine ShipHero data with API data without writing a single line of code. --- # How to combine ShipHero data with Excel data Source: https://parabola.io/tool/how-to-combine-shiphero-data-with-excel-data Combine ShipHero data with Excel data without writing a single line of code. --- # How to combine ShipHero data with Google Sheets data Source: https://parabola.io/tool/how-to-combine-shiphero-data-with-google-sheets-data Combine ShipHero data with Google Sheets data without writing a single line of code. --- # How to combine ShipHero data with PDF file data Source: https://parabola.io/tool/how-to-combine-shiphero-data-with-pdf-file-data Combine ShipHero data with PDF file data without writing a single line of code. --- # How to combine Shopify data with API data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-api-data Combine Shopify data with API data without writing a single line of code. --- # How to combine Shopify data with Email data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-email-data Combine Shopify data with Email data without writing a single line of code. --- # How to combine Shopify data with Excel data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-excel-data Upload your Excel Doc, connect to Shopify, and use this ready workflow to merge both datasets — no coding required. --- # How to combine Shopify data with Google Sheets data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-google-sheets-data Combine Shopify data with Google Sheets data without writing a single line of code. --- # How to combine Shopify data with PDF file data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-pdf-file-data Combine Shopify data with PDF file data without writing a single line of code. --- # How to integrate Shopify data with Salesforce data using AI Source: https://parabola.io/tool/how-to-integrate-shopify-with-salesforce Automatically integrate Shopify and Salesforce data without writing a single line of code. --- # How to combine Shopify data with ShipHero data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-shiphero-data Combine Shopify data with ShipHero data without writing a single line of code. --- # Combine Snowflake and Amazon Seller Central data using AI Source: https://parabola.io/tool/combine-snowflake-and-amazon-seller-central-data-using-ai Combine Amazon Seller Central with Snowflake to centralize sales, fees, and payout data, automate reconciliations, and power deeper e-commerce analytics. --- # Combine Snowflake and Shopify data using AI Source: https://parabola.io/tool/combine-snowflake-and-shopify-data-using-ai Sync Shopify data with Snowflake to power advanced analytics, automate reporting, and combine e-commerce sales with all your business data in one place. --- # Compare AI & automation tools side by side Source: https://parabola.io/tool/compare-ai-automation-tools-side-by-side Compare the top AI automation platforms in one place. Review features, pricing, and use cases to find the best fit for your team. --- # Use AI to convert data from a bill of lading to a spreadsheet Source: https://parabola.io/tool/how-to-use-ai-to-convert-data-from-a-bill-of-lading-to-a-spreadsheet This prebuilt workflow lets you upload any BOL and extract rows, columns, and tables into a clean dataset. --- # Use AI to convert data from a CBP Form 7501 to a spreadsheet Source: https://parabola.io/tool/use-ai-to-convert-data-from-a-cbp-form-7501-to-a-spreadsheet Convert data from a CBP Form 7501 to a spreadsheet without writing a single line of code. --- # Use AI to convert data from commercial invoice PDFs to spreadsheets Source: https://parabola.io/tool/use-ai-to-convert-data-from-commercial-invoice-pdfs-to-spreadsheets Convert data from commercial invoice PDFs to spreadsheets without writing a single line of code. --- # Use AI to convert data from freight invoice PDFs to spreadsheets Source: https://parabola.io/tool/use-ai-to-convert-data-from-freight-invoice-pdfs-to-spreadsheets Convert data from freight invoice PDFs to spreadsheets without writing a single line of code. --- # Use AI to convert data from packing lists to spreadsheets Source: https://parabola.io/tool/use-ai-to-convert-data-from-packing-lists-to-spreadsheets Convert data from packing lists to spreadsheets without writing a single line of code. --- # Use AI to convert data from a PDF to a spreadsheet Source: https://parabola.io/tool/use-ai-to-convert-data-from-a-pdf-to-a-spreadsheet This prebuilt workflow lets you upload any PDF and extract rows, columns, and tables into a clean dataset. --- # Use AI to convert data from purchase order PDFs to spreadsheets Source: https://parabola.io/tool/use-ai-to-convert-purchase-order-pdfs-to-spreadsheets Convert data from purchase order PDFs to spreadsheets without writing a single line of code. --- # How to count days between dates within your Amazon Seller Central data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-amazon-seller-central-data Count days between dates within your Amazon Seller Central data without writing a single line of code. --- # How to count days between dates within your CSV data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-csv-data Count days between dates within your CSV data without writing a single line of code. --- # How to count days between dates within your Excel data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-excel-data Count days between dates within your Excel data without writing a single line of code. --- # How to count days between dates within your Google Sheets data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-google-sheets-data Count days between dates within your Google Sheets data without writing a single line of code. --- # How to count days between dates within your NetSuite data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-netsuite-data Count days between dates within your Netsuite data without writing a single line of code. --- # How to count days between dates within your ShipHero data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-shiphero-data Count days between dates within your ShipHero data without writing a single line of code. --- # How to count days between dates within your Shopify data Source: https://parabola.io/tool/how-to-count-days-between-dates-within-your-shopify-data Count days between dates within your Shopify data without writing a single line of code. --- # How to use AI to automatically extract your Amazon Redshift data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-amazon-redshift-data Automatically extract your Amazon Redshift data without writing a single line of code. --- # How to use AI to automatically extract your Amazon Seller Central data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-amazon-seller-central-data Automatically extract your Amazon Seller Central data without writing a single line of code. --- # How to use AI to automatically extract your API data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-api-data Automatically extract your API data without writing a single line of code. --- # How to use AI to automatically extract your CSV data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-csv-data Automatically extract your CSV data without writing a single line of code. --- # How to use AI to automatically extract data from emails Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-email-data Upload your emails and let AI automatically extract sender info, body content, and attachment data into structured output. --- # How to use AI to automatically extract your Excel data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-excel-data Use this template to automatically pull data from Excel files with AI. --- # How to use AI to automatically extract your Google Analytics data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-google-analytics-data Automatically extract your Google Analytics data without writing a single line of code. --- # How to use AI to automatically extract your Google Sheets data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-google-sheets-data Automatically extract your Google Sheets data without writing a single line of code. --- # How to use AI to automatically extract your Hubspot data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-hubspot-data Automatically extract your Hubspot data without writing a single line of code. --- # How to use AI to automatically extract your Looker data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-looker-data Automatically extract your Looker data without writing a single line of code. --- # How to use AI to automatically extract your Netsuite data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-netsuite-data Automatically extract your Netsuite data without writing a single line of code. --- # How to use AI to automatically extract your PDF data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-pdf-data Use this template to automatically pull data from PDF files with AI. Simplify document parsing and export structured results instantly. --- # How to use AI to automatically extract your Salesforce data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-salesforce-data Automatically extract your Salesforce data without writing a single line of code. --- # How to use AI to automatically extract your ShipHero data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-shiphero-data Automatically extract your ShipHero data without writing a single line of code. --- # How to use AI to automatically extract your Shopify data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-shopify-data Automatically extract your Shopify data without writing a single line of code. --- # How to use AI to automatically extract your Stripe data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-extract-your-stripe-data Automatically extract your Stripe data without writing a single line of code. --- # How to find and replace values within your Amazon Seller Central data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-amazon-seller-central-data Find and replace values within your Amazon Seller Central data without writing a single line of code. --- # How to find and replace values within your CSV data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-csv-data Find and replace values within your CSV data without writing a single line of code. --- # How to find and replace values within your Excel data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-excel-data Find and replace values within your Excel data without writing a single line of code. --- # How to find and replace values within your Google Sheets data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-google-sheets-data Find and replace values within your Google Sheets data without writing a single line of code. --- # How to find and replace values within your NetSuite data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-netsuite-data Find and replace values within your Netsuite data without writing a single line of code. --- # How to find and replace values within your ShipHero data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-shiphero-data Find and replace values within your ShipHero data without writing a single line of code. --- # How to find and replace values within your Shopify data Source: https://parabola.io/tool/how-to-find-and-replace-values-within-your-shopify-data Find and replace values within your Shopify data without writing a single line of code. --- # Quickly and securely turn your CSV into anonymized, demo-safe data Source: https://parabola.io/tool/data-anonymization Use our fast and secure anonymization tool to safely strip any CSV of sensitive data. Specify column formats, generate sample data with AI, and generate clean CSV files. --- # How to Automate Account Assignment in Salesforce Source: https://parabola.io/tool/how-to-automate-account-assignment-in-salesforce Automatically assign accounts to the right reps or account managers in Salesforce with data-driven routing logic powered by Parabola. --- # How to combine Shopify data with Amazon Seller Central data Source: https://parabola.io/tool/how-to-combine-shopify-data-with-amazon-seller-central-data Combine Shopify data with Amazon Seller Central data without writing a single line of code. --- # How to use AI to automatically standardize your Amazon Seller Central data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-amazon-seller-central-data Automatically standardize your Amazon Seller Central data without writing a single line of code. --- # How to use AI to automatically standardize your ShipHero data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-shiphero-data Automatically standardize your ShipHero data without writing a single line of code. --- # How to use AI to automatically standardize your Excel data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-excel-data Automatically standardize your Excel data without writing a single line of code. --- # How to use AI to automatically standardize your Google Sheets data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-google-sheets-data Automatically standardize your Google Sheets data without writing a single line of code. --- # How to use AI to automatically standardize your Netsuite data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-netsuite-data Automatically standardize your Netsuite data without writing a single line of code. --- # How to use AI to automatically standardize your PDF data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-pdf-data Automatically standardize your PDF data without writing a single line of code. --- # How to use AI to automatically standardize your Shopify data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-shopify-data Automatically standardize your Shopify data without writing a single line of code. --- # How to remove duplicate rows or values from your Amazon Seller Central data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-amazon-seller-central-data Remove duplicate rows or values from your Amazon Seller Central data without writing a single line of code. --- # How to remove duplicate rows or values from your CSV data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-csv-data Remove duplicate rows or values from your CSV data without writing a single line of code. --- # How to remove duplicate rows or values from your Excel data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-excel-data Remove duplicate rows or values from your Excel data without writing a single line of code. --- # How to remove duplicate rows or values from your Google Sheets data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-google-sheets-data Remove duplicate rows or values from your Google Sheets data without writing a single line of code. --- # How to remove duplicate rows or values from your PDF data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-pdf-data Remove duplicate rows or values from your PDF data without writing a single line of code. --- # How to remove duplicate rows or values from your ShipHero data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-shiphero-data Remove duplicate rows or values from your ShipHero data without writing a single line of code. --- # How to remove duplicate rows or values from your Shopify data Source: https://parabola.io/tool/how-to-remove-duplicate-rows-or-values-from-your-shopify-data Remove duplicate rows or values from your Shopify data without writing a single line of code. --- # Sync LinkedIn Engagement Data to Salesforce Accounts Source: https://parabola.io/tool/sync-linkedin-engagement-data-to-salesforce-accounts Automatically connect LinkedIn engagement data to Salesforce accounts to track performance and prioritize high-interest prospects. --- # How to transpose and pivot your Amazon Seller Central data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-amazon-seller-central-data Transpose and pivot your Amazon Seller Central data without writing a single line of code. --- # How to transpose and pivot your CSV data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-csv-data Transpose and pivot your CSV data without writing a single line of code. --- # How to transpose and pivot your Excel data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-excel-data Transpose and pivot your Excel data without writing a single line of code. --- # How to transpose and pivot your Netsuite data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-netsuite-data Transpose and pivot your Netsuite data without writing a single line of code. --- # How to transpose and pivot your ShipHero data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-shiphero-data Transpose and pivot your ShipHero data without writing a single line of code. --- # How to transpose and pivot your Shopify data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-shopify-data Transpose and pivot your Shopify data without writing a single line of code. --- # How to transpose and pivot your Google Sheets data Source: https://parabola.io/tool/how-to-transpose-and-pivot-your-google-sheets-data Transpose and pivot your Google Sheets data without writing a single line of code. --- # Turn your Parabola flow into LinkedIn content with AI Source: https://parabola.io/tool/turn-your-parabola-flow-into-linkedin-content-with-ai Most ops wins go unnoticed—but they shouldn’t. Turn behind-the-scenes work into front-and-center stories with Parabola’s flow-to-content tool. --- # How to use AI to automatically categorize your Amazon Seller Central data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-categorize-your-amazon-seller-central-data Automatically categorize your Amazon Seller Central data without writing a single line of code. --- # How to use AI to automatically categorize your Email data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-categorize-your-email-data Use this prebuilt AI workflow to automatically classify email content into categories. Upload your emails and let AI segment them by topic or type. --- # How to use AI to automatically categorize your Excel data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-categorize-your-excel-data Automatically categorize your Excel data without writing a single line of code. --- # How to use AI to automatically categorize your Netsuite data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-categorize-your-netsuite-data Automatically categorize your Netsuite data without writing a single line of code. --- # How to use AI to automatically categorize your PDF data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-categorize-your-pdf-data Automatically categorize your PDF data without writing a single line of code. --- # How to use AI to automatically standardize your API data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-api-data Automatically standardize your API data without writing a single line of code. --- # How to use AI to automatically standardize your CSV data Source: https://parabola.io/tool/how-to-use-ai-to-automatically-standardize-your-csv-data Automatically standardize your CSV data without writing a single line of code. --- # Prompting best practices Source: https://parabola.io/resources/parabola-university/ai-fundamentals-prompting-best-practices Learn how to write clear, effective prompts to get the most out of Parabola’s AI chat and Custom transform step. In this session, the Parabola team shares best practices, real-world examples, and practical tips for turning plain language into powerful, production-ready workflows. Whether you're building reports, transforming data, or automating exports, this guide will help you use AI to move faster and build with confidence. ## Topics covered - How Parabola interprets your prompts - Writing high-quality prompts for Custom Transform - Formatting, logic, and edge case considerations - Real prompt examples and breakdowns - Tips from Parabola’s product and engineering teams For more, explore Parabola's [prompting best practices guide](https://parabola.io/product/overview/using-ai-chat-to-build-and-edit-flows#best-practices-writing-effective-prompts). --- # Building with Parabola's AI chat interface Source: https://parabola.io/resources/parabola-university/building-fundamentals-parabolas-ai-chat-interface While this course focuses on the fundamentals of Parabola, you'll immediately notice that there's a new, AI-first way to build in Parabola—and it's incredibly powerful. In this video, we introduce you to Parabola's chat interface, share some example prompts, and highlight prompting best practices. ## Building challenge The building challenge will begin when you get to the first "Pulling data" lesson. If you have any questions on the challenge as you build, feel free to ask Parabola. --- # Building challenge Source: https://parabola.io/resources/parabola-university/building-challenge The absolute best way to learn Parabola is by getting hands-on experience in the tool — and Parabola University’s building challenges allow you to do just that. ## To get started with the building challenge If you haven't already,****[click this button to create a Flow](https://parabola.io/api/clipboard/4875bb22-d42c-40f7-9681-466b0e1e7807/copy_to_flow?name=Parabola+University)**which we’ll use for the duration of the course.** #### Once you complete the challenges, you’ll walk away with a complete Flow and dashboard Dive into the challenge in the next lesson. --- # Intro to Parabola University & basics Source: https://parabola.io/resources/parabola-university/building-fundamentals-course-overview Welcome to Parabola University! This ~1 hour interactive course will teach you the fundamental skills you need to start automating manual processes in Parabola. ### Building challenge The best way to learn Parabola is by getting hands-on experience in the tool. By completing the challenges associated with each lesson, you’ll walk away having completed a full sales reporting workflow and dashboard: **To follow along with the building challenge,**[click this button to create a Flow](https://parabola.io/api/clipboard/4875bb22-d42c-40f7-9681-466b0e1e7807/copy_to_flow?name=Parabola+University)**which we’ll use for the duration of the course. ** Ready to learn the fundamentals? Start the next lesson to learn about the building blocks of Parabola. --- # Parabola basics Source: https://parabola.io/resources/parabola-university/building-fundamentals-parabola-basics You can build just about any type of data automation you can imagine in Parabola — from processes that update dashboards in real time to automations that can update your Shopify instance or send an email alert to your team at a regular cadence. There are four fundamental components to building Parabola Flows: 1. Steps that pull data in from sources like emails, PDFs, databases, systems like Netsuite and Shopify, or wherever else you get data 1. Steps that transform data like Edit Columns, Remove Duplicates, or Add Math Column 1. Steps that send data out to destinations like email, Slack, Parabola Tables, and databases 1. Cards to document processes** ** Now that you’re familiar with the basics of Parabola, let’s dive into step 1: pulling in data. --- # Exporting data: key concepts Source: https://parabola.io/resources/parabola-university/key-concepts-exporting-data Now that we’ve completed our Parabola Flow, it’s time to run our Flow and action on our data. ### Building challenge In this challenge, we’ll export our data via email. - To the right of the card titled “Categorize products with AI”, add a new card titled “Trigger an email” and add an **Email a file attachment**step to the card - Within the **Email a file attachment**step… Enter your personal email in the Email Recipients field - Make the subject: Parabola University Demo - Make the email body: Demo Click the Run Flow buttonFind the email in your inbox #### [Check your work](https://www.notion.so/parabola/Exporting-data-key-concepts-2a7895a0a0de809e8cc5fe7814a17fe2) ‍ --- # LinkedIn certification Source: https://parabola.io/resources/parabola-university/linkedin-certification Congratulations! Now that you know the basics of Parabola, you’re ready to begin building on your own. Before you go: We recommend you **highlight your new skills by adding a certification on LinkedIn. ** ### Receive your certificate To receive your Parabola University Building Foundations certificate (*and optionally, leave feedback on the course*), please fill out [this 30-second Google Form](https://docs.google.com/forms/d/e/1FAIpQLSdW_DzezkUbc8i45iYhxV3rRT-BeBLj45A0_z0aJQPeA319vw/viewform) and a member of our team will follow up with your certificate. Thank you for completing Parabola University! --- # Parabola Tables Source: https://parabola.io/resources/parabola-university/exporting-data-parabola-tables In this lesson, we dive into dashboards and the core concepts of Tables — databases that live in Parabola and power dashboards and visualizations. ## Building challenge In this challenge, we’ll create a Table that is grouped by status and warehouse to make it easy for the team to review SKUs with low inventory levels. - In the Inventory report Table, group rows first based on status, then by warehouse. - Change the color of the Table to green, and freeze the first column of data. - Add a calculation to the Available column and sum up units available by status. Format the column to include a comma. To check your work, take a look at [this quick video](https://www.loom.com/share/4344b7e3b571482ab1207b5aa7e93407). ## About Parabola Tables With Parabola Tables, you can - Store up to 5 million rows and 5,000 columns of data - Share data across Flows using the [Pull from Parabola Table](https://parabola.io/integration/table#pull-from-parabola-table) step - Build interactive data Tables with groupings, custom views, and heat maps - Use Table data to power additional visualizations ## Customizing your Parabola Table There are a few essential pieces of functionality you should know about when working with Parabola Tables: - **Grouping**: The grouping function allows you to create pivot Table-like views with dropdowns. - **Calculations:**Click on either a grouped row or the bottom bar of your Parabola Table to add calculated fields to your Parabola Table.Ex) After grouping your Inventory data by Status, you can Sum all of the values in the Available column to see an overview of quantities by status. Colors: Beyond customizing the color of your Table, you can also add color to specific columns — including color scales and color rules. Hiding: Select columns to hide from certain views. --- # Parabola tables & visualizations Source: https://parabola.io/resources/parabola-university/exporting-data-dashboards-and-visualizations In this lesson, we dive into dashboards and visualizations and the core concepts behind Tables — databases that live in Parabola. ### Building challenge | Part 1 (Tables) - Above your “Trigger an email” card, add a card titled “Create a Parabola Table” and add a **Send to Parabola Table** step to the card - Within the step, name your table “Parabola University Sales Data”, and keep the “Overwrite the table” setting as-is - Hit “Run Flow” to populate the Table with data #### [Check your work](https://www.notion.so/parabola/Parabola-Tables-Visualizations-2a7895a0a0de80f6b706f49d1b0575fd) ‍ ### Building challenge | Part 2 (Visualizations) In this challenge, we’ll create a visualization on the canvas to look at sales over time. - Below your “Trigger an email” card, add a card titled, “Visualize sales over time” and add a **Visualize** step to the card - Draw an arrow from **Categorize with AI**to **Visualize**and click “Edit this view” - In the top left, change the view from a Table to a Column chart - Name the chart “Sales over time”, and make the X-axis Order date - Under Y-axis, click “+ Add series”, then select Revenue (also change Count all to Sum) - Bonus: In your X- and Y-axis options, name your axes “Month” and “Revenue” respectively and format your Y-axis as currency #### [Check your work](https://www.notion.so/parabola/Parabola-Tables-Visualizations-2a7895a0a0de80f6b706f49d1b0575fd) ‍ ‍ --- # Running flows automatically Source: https://parabola.io/resources/parabola-university/exporting-data-running-flows-automatically Once you’ve completed your Flow and set up live integrations, you’re ready to set your Flow on an automated schedule. ### Building challenge There is no building challenge for this lesson — continue onto the next page to officially wrap up the course and receive your course certificate. --- # Additional resources Source: https://parabola.io/resources/parabola-university/finance-learning-pathway-additional-resources ### Looking for more building support? [Parabola’s AI chat interface](https://parabola.io/resources/parabola-university/building-fundamentals-parabolas-ai-chat-interface) makes building and editing workflows faster and easier by letting you describe what you want to do in plain language. Try asking it to clean data, join tables, create logic steps, or troubleshoot issues—no technical expertise needed. It can also explain how flows work, suggest improvements, and even build full sections of your workflow for you. It’s like having a co-pilot for operations automation. ### Need more help? - Email us at [support@parabola.io](mailto:support@parabola.io) - Chat with us in-app via the "Help" widget ### Work with an expert Need hands-on support? Tap into our [network of certified Parabola Experts](https://parabola.io/experts). In the network, you'll find: - **Parabola Experts:** Power users who are ready to help you build, scale, and unlock value fast. - **Strategic Experts:** Trusted advisors who bring clarity, guide your ops strategy, and help you scale smarter. - **Fractional Experts:** Execution-first operators who build processes, streamline systems, and get things moving. - **Hands-on Partners:** Embedded experts who lead critical ops work without the need for a full-time hire. --- # Getting started Source: https://parabola.io/resources/parabola-university/finance-learning-pathway-getting-started To start automating flows in Parabola, we recommend you complete three key steps: ### Step 1: Account setup - ✅ [Invite your team members](https://parabola.io/app/team) to collaborate across departments. - 🗂️ [Create folders in your team’s flow section](https://parabola.io/app/flows/team-flows) to organize by use case or team. ### Step 2: Complete Parabola University ⭐️ [Parabola University's Building fundamentals course](https://parabola.io/resources/parabola-university/building-fundamentals-course-overview) is our #1 learning resource, **highly-recommended for all new users**. The <1 hour course gives you hands-on experience building flows and understanding key concepts like joining data, filtering, logic building, and outputting results. ### Step 3: Build your first use case with templates Next, we'll leverage our [template library](https://parabola.io/use-cases) to build our first flow (and on the next page, you'll learn about the three we recommend building first). Before choosing your first use case, it's helpful to understand what makes a good Parabola use case: - **Varied, unstructured, and dynamic data:** Data coming in multiple formats, often from multiple sources. That data might be difficult to access or parse like emails, PDFs, internal systems, WMS, TMS, ERP or data warehouses. - **Recurring processes at scale:** Processes occurring multiple times per day/week - often involving multiple people. The work is repetitive and susceptible to errors. - **SOPs and logic-based systems:** Often where teams are breaking into spreadsheets that could contain errors or outdated data. - **Collaboration across teams and 3rd parties:** Instances where you are waiting on or sending data to other parties inside or outside your organization. - **Drive quantifiable business outcomes:** Help with cost avoidance, revenue acceleration, and alignment with strategic, company wide initiatives. Next, we’ll cover three use cases that often meet this criteria and make for great initial Parabola use cases. ‍ --- # Introduction Source: https://parabola.io/resources/parabola-university/finance-learning-pathway-introduction Welcome to Parabola 👋 Month-end always comes faster than expected—and finance teams are always expected to do more with less. Between consolidations, reconciliations, and chasing down last-minute entries, there’s rarely time to step back and improve process to ultimately improve visibility and reduce your time to close. ### Parabola is built to save you time, automate data cleanup, and speed up your close Beyond time savings, Parabola simplifies and automates manual workflows, helping you reduce errors and scale processes. Here's how it works: ### Learn about key Parabola functionality ▼ - **Connect to every system in your stack:** Whether your data lives in Shopify, ShipHero, NetSuite, or Looker, automatically pull in data from any data source - **Extract and standardize messy data:** Use AI to pull data directly from your email, including details from email bodies and attachments - **Automate and standardize complex logic:** In Parabola, you can do anything you can do in a spreadsheet - **Integrate AI:** Want to use AI, but not sure where to start? Parabola offers steps to standardize, extract, categorize, and transform your data with AI - **Set up alerts, dashboards, and integrations:** Parabola sends your data wherever it needs to go—whether that's an email, Slack message, NetSuite update, or dashboard - **Document process:** Parabola flows become real-time process documentation - **Learn to build in seconds:** Parabola flows are built using plain language. If you can explain your process in words, you can build in Parabola ‍ ### Automate key processes across finance and accounting By pulling data from all your tools and automating the cleanup and transformation that usually happens in spreadsheets, Parabola supports processes like: - **Preparing journal entries**based on actuals coming from siloed systems - **Variance analysis**by comparing your forecast vs. actuals instantly - **Estimating accruals**accurately by enabling bottoms-up expense estimates Plus thousands of additional use cases. ### Ready to get started? Keep reading for actionable tips, educational resources, building exercises and more to start automating processes quickly. --- # Use case exercises Source: https://parabola.io/resources/parabola-university/finance-learning-pathway-use-cases After completing Parabola University, the best way to learn Parabola is by automating your first use case. Based on experience with hundreds of similar operators, we recommend starting with one of the following three use cases: Accrual calculations ##### What is an accrual calculation? An [accrual calculation](https://parabola.io/use-cases/accrual-calculations) estimates expenses or revenues that have been incurred but not yet recorded—ensuring financials reflect the true state of the business for a given period. ##### How does Parabola support accrual calculations? With Parabola, you can take a bottoms-up approach to accrual calculations by blending data from multiple systems to estimate actual expenses. For instance, you might combine shipment data with your 3PL's rate card to estimate your invoice for the month. **Want to see the use case in action? **[Check out this video overview](https://youtu.be/1Ka5SvM_nH0?si=ryCx7eAAX0o6_URF)**.** ### How to automate accrual calculations ▼ How to build 1. Pull datasets that will help identify expenses that were accrued but not invoiced for using steps like **Pull from CSV file** and **Pull from NetSuite**. 1. Clean the dataset and remove unnecessary column values using steps like **Edit columns** and **Filter rows**. 1. Compare datasets to identify expenses that were incurred but not invoiced for using steps like **Combine tables** (like a VLOOKUP) and **Compare tables**. 1. Apply math calculations and aggregate data using steps like **Add math column** and **Sum by group** to calculate total accrued expenses. 1. Assign values to GL accounts using steps like **Add text column** and **Add if/else column**. 1. Prepare your data to be uploaded back into your ERP or any other system using steps like **Format numbers** and **Edit columns**. 1. Export the final dataset using steps like **Generate CSV file**, **Send to Google Sheets**, or **Email a file attachment**. #### Tips - Standardize data early using an **Edit columns** or **Standardize with AI** step to avoid discrepancies in naming conventions. - Use validation steps like **Remove duplicates** and **Find overlap** to check for duplicate or missing accrual records. - Automate journal entry formatting by dynamically assigning GL accounts with an **Add if/else column** step. - Build a historical accrual tracking table in Parabola to analyze trends and refine calculations over time. Variance analysis ##### What is variance analysis? [Variance analysis](https://parabola.io/use-cases/budget-vs-actual-reporting) is the process of comparing actuals against forecasts or budgets to understand where and why performance deviated. ##### How does Parabola support variance analysis? With Parabola, you can pull actuals from your ERP or accounting system, join them with forecast or budget data from spreadsheets or planning tools, and calculate variances across departments, accounts, or time periods. Build automated flows to highlight material variances, trigger alerts, and feed insights into dashboards or close decks—no manual number-crunching required. ### How to automate variance analysis ▼ How to build 1. Pull budget and forecast data from spreadsheets, ERPs, or financial planning tools using steps like **Pull from Google Sheets** or **Pull from CSV file**. 1. Pull actual financial data from your accounting system, payment processor, or bank statements using steps like **Pull from PDF**, **Pull from CSV**, or **Pull from NetSuite**. 1. Standardize and clean the data using steps like **Edit columns**, **Find and replace**, and **Format dates** to ensure consistent formatting. 1. Use the **Combine tables** step to merge budgeted and actual data based on common identifiers such as account codes, categories, or time periods. 1. Calculate budget variances by applying the **Add math column** step to determine the percentage difference between budgeted and actual values. 1. Flag significant variances using the **Add if/else column** step to highlight overages or underspending beyond a set threshold. 1. Generate automated reports for finance teams by adding an **Email a file attachment** step, or push data to a place like Google Sheets or an external system. 1. Optionally, push data into a **Visualize** step to create a reporting dashboard for ongoing monitoring and forecast adjustments. #### Tips - If actual spend data is trapped in PDF invoices, you can use Parabola's PDF parsing steps to automatically extract data from PDF invoices. - Ensure budget and actual data are categorized consistently to avoid mismatches — standardize account names, time periods, and currency formats early in the flow. - Automate variance flagging with conditional logic to highlight categories where spending is significantly over or under budget. - Schedule your Parabola Flow to run automatically, keeping budget tracking up-to-date without manual intervention. - Use historical budget vs. actual trends to refine forecasting models, adjusting budgets based on past performance patterns. Freight and parcel invoice audit ##### What is a freight and parcel invoice audit? [Freight and parcel invoice auditing](https://parabola.io/blog/freight-audit-process) is the process of ingesting invoices from various carriers and auditing them against the carrier’s rate card to identify discrepancies in fuel surcharges, line items, accessorial fees, and other charges. This ensures billing accuracy and prevents overpayments. Freight and parcel invoice audits can be challenging because of the consolidation and standardization required across carrier portals, PDFs, and emails, since every carrier formats their data differently. ##### How does Parabola support freight and parcel invoice audits? With Parabola, you can parse PDF invoices from carriers and ingest invoice data via invoice, match charges with your rate card, calculate invoicing overages, and trigger alerts for discrepancies above a certain threshold. **Want to see the use case in action? **[Check out this video overview](https://youtu.be/8gOpklp2dpE?si=kEp0mcr8ghwBpNAt)**.** ### How to automate a freight and parcel invoice audit ▼ How to build 1. Pull invoice data from sources like PDFs, emails, carrier portals, and/or CSV files using steps like [Pull from inbound email](https://parabola.io/product/integration/email-a-file). 1. Clean and standardize the data using steps like **Edit columns**, [Extract with AI](https://parabola.io/product/transform/extract-with-ai), and [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai) to normalize values and match formatting across your invoices and rate cards. 1. Using a [Combine tables](https://parabola.io/product/transform/combine-tables) step, join the datasets based on identifiers such as shipping method, bill weight, unit of measure, and pricing zone. 1. Calculate discrepancies by applying math formulas to compare the invoice data against the rate card using an [Add math column](https://parabola.io/product/transform/insert-math-column) step, or use the AI-enabled [Custom transform](https://parabola.io/product/transform/custom-transform) step for complex calculations. 1. Action on discrepancies with the [Email a file attachment](https://parabola.io/product/integration/email-attachment) step by setting up email notifications for discrepancies exceeding a defined threshold, such as 3% or a specific dollar amount. 1. Create a dashboard or visualization in Parabola to track discrepancies across carriers and shipping methods. 1. Optionally, use this data in a carrier scorecard Flow to monitor billing accuracy as a performance metric. #### Tips - The simplest way to get started on a freight or parcel audit, before integrating via email or connecting an API, is often with a static file — using a step like [Pull from CSV file](https://parabola.io/product/integration/csv-file) or [Pull from PDF file](https://parabola.io/product/integration/pdf-file). - Standardize carrier names, shipping methods, and accessorials early in your flow using steps like **Edit columns**, [Extract with AI](https://parabola.io/product/transform/extract-with-ai), and **Standardize with AI**. - Consider standardizing and combining rate sheets for a single carrier at the beginning of your flow using a **Stack tables** step to streamline comparisons. - Create visualizations to monitor billing accuracy across carriers and identify discrepancies by shipping service. With some building experience under your belt, continue reading for more building resources. --- # Additional resources Source: https://parabola.io/resources/parabola-university/operations-learning-pathway-additional-resources ### Looking for more building support? [Parabola’s AI chat interface](https://parabola.io/resources/parabola-university/building-fundamentals-parabolas-ai-chat-interface) makes building and editing workflows faster and easier by letting you describe what you want to do in plain language. Try asking it to clean data, join tables, create logic steps, or troubleshoot issues—no technical expertise needed. It can also explain how flows work, suggest improvements, and even build full sections of your workflow for you. It’s like having a co-pilot for operations automation. ### Need more help? - Email us at [support@parabola.io](mailto:support@parabola.io) - Chat with us in-app via the "Help" widget ### Work with an expert Need hands-on support? Tap into our [network of certified Parabola Experts](https://parabola.io/experts). In the network, you'll find: - **Parabola Experts:** Power users who are ready to help you build, scale, and unlock value fast. - **Strategic Experts:** Trusted advisors who bring clarity, guide your ops strategy, and help you scale smarter. - **Fractional Experts:** Execution-first operators who build processes, streamline systems, and get things moving. - **Hands-on Partners:** Embedded experts who lead critical ops work without the need for a full-time hire. --- # Getting started Source: https://parabola.io/resources/parabola-university/operations-learning-pathway-getting-started To start automating flows in Parabola, we recommend you complete three key steps: ### Step 1: Account setup - ✅ [Invite your team members](https://parabola.io/app/team) to collaborate across departments. - 🗂️ [Create folders in your team’s flow section](https://parabola.io/app/flows/team-flows) to organize by use case or team. ### Step 2: Complete Parabola University ⭐️ [Parabola University's Building fundamentals course](https://parabola.io/resources/parabola-university/building-fundamentals-course-overview) is our #1 learning resource, **highly-recommended for all new users**. The <1 hour course gives you hands-on experience building flows and understanding key concepts like joining data, filtering, logic building, and outputting results. ### Step 3: Build your first use case with templates Next, we'll leverage our [template library](https://parabola.io/use-cases) to build our first flow (and on the next page, you'll learn about the three we recommend building first). Before choosing your first use case, it's helpful to understand what makes a good Parabola use case: - **Varied, unstructured, and dynamic data:** Data coming in multiple formats, often from multiple sources. That data might be difficult to access or parse like emails, PDFs, internal systems, WMS, TMS, ERP or data warehouses. - **Recurring processes at scale:** Processes occurring multiple times per day/week - often involving multiple people. The work is repetitive and susceptible to errors. - **SOPs and logic-based systems:** Often where teams are breaking into spreadsheets that could contain errors or outdated data. - **Collaboration across teams and 3rd parties:** Instances where you are waiting on or sending data to other parties inside or outside your organization. - **Drive quantifiable business outcomes:** Help with cost avoidance, revenue acceleration, and alignment with strategic, company wide initiatives. Next, we’ll cover three use cases that often meet this criteria and make for great initial Parabola use cases. ‍ --- # Introduction Source: https://parabola.io/resources/parabola-university/operations-learning-pathway-introduction Welcome to Parabola 👋 If there’s one thing we know about operators, it’s that bandwidth is always tight. Teams are spread thin managing critical supply chain processes, inventory systems, fulfillment workflows, and reconciliations—all before lunch. ### Parabola is built to save you time and automate your manual processes Beyond time savings, Parabola simplifies and automates manual operational workflows, helping you reduce errors and scale processes. Here's how it works: ### Learn about key Parabola functionality ▼ - **Connect to every system in your stack:** Whether your data lives in Shopify, ShipHero, NetSuite, or Looker, automatically pull in data from any data source - **Extract and standardize messy data:** Use AI to pull data directly from your email, including details from email bodies and attachments - **Automate and standardize complex logic:** In Parabola, you can do anything you can do in a spreadsheet - **Integrate AI:** Want to use AI, but not sure where to start? Parabola offers steps to standardize, extract, categorize, and transform your data with AI - **Set up alerts, dashboards, and integrations:** Parabola sends your data wherever it needs to go—whether that's an email, Slack message, NetSuite update, or dashboard - **Document process:** Parabola flows become real-time process documentation - **Learn to build in seconds:** Parabola flows are built using plain language. If you can explain your process in words, you can build in Parabola ‍ ### Automate key processes across inventory and transportation operations By pulling data from all your tools and automating the cleanup and transformation that usually happens in spreadsheets, Parabola supports processes like: - **Automating inventory reconciliation** across your sales channels, WMS, and ERP - **Streamlining freight audit** **processes** to catch discrepancies - **Consolidating inbound shipments** from carriers and flag delays Plus thousands of additional use cases. ### See how leading brands use Parabola - How Bandit’s ops team reclaimed 10+ hours a week & built real-time visibility with Parabola ([link](https://parabola.io/case-studies/bandit)) - How Rhone doubled their operational capacity with Parabola ([link](https://parabola.io/case-studies/rhone)) - How Caraway Home gets 150 hrs/month back by automating their supply chain ops ([link](https://parabola.io/case-studies/caraway)) ### Ready to get started? Keep reading for actionable tips, educational resources, building exercises and more to start automating processes quickly. --- # Use case exercises Source: https://parabola.io/resources/parabola-university/operations-learning-pathway-use-cases After completing Parabola University, the best way to learn Parabola is by automating your first use case. Based on experience with hundreds of similar operators, we recommend starting with one of the following three use cases: Inventory reconciliation ##### What is an inventory reconciliation? [Inventory reconciliation](https://parabola.io/blog/inventory-reconciliation-and-reporting) is the process of comparing inventory levels across siloed systems (like your ERP, WMS, and sales channels like Shopify) to identify discrepancies and ultimately update systems. This prevents stockouts, mitigates negative customer experiences, and enables proactive inventory decision-making. ##### How does Parabola support inventory reconciliation? With Parabola, you can connect directly to every inventory channel in your stack, match SKUs across systems, calculate discrepancies, and trigger alerts for discrepancies above a certain threshold. **Want to see the use case in action? **[Check out this video overview](https://youtu.be/317G9wxVHjk?si=_eoEIdGDGtOjgJZn)**.** ### How to automate an inventory reconciliation ▲ How to build 1. Pull inventory data from systems like your ERP, WMS, and/or sales channels using steps like [Pull from Shopify](https://parabola.io/product/integration/shopify#pull-from-shopify) or [Pull from NetSuite](https://parabola.io/product/integration/netsuite) 1. Standardize the data by cleaning SKU values and renaming columns using steps like **Edit columns**, [Extract with AI](https://parabola.io/product/transform/extract-with-ai), and [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai) 1. Using the [Combine tables](https://parabola.io/product/transform/combine-tables) step, join the datasets by merging based on shared identifiers such as SKU and warehouse location 1. Calculate discrepancies by applying the [Add math column](https://parabola.io/product/transform/insert-math-column) step to compute and identify mismatches between inventory values across systems 1. Use the [Add if/else column](https://parabola.io/product/transform/insert-if-else-column) step to assign statuses to categorize records, creating a column for statuses such as 'Validated' or 'Discrepancy Detected' to flag issues 1. Alert the team of discrepancies by adding an [Email a file attachment](https://parabola.io/product/integration/email-attachment) step or triggering a Slack message 1. Ensure visibility into inventory health by creating a real-time dashboard using a **Visualize** step at the end of your Flow #### Tips - Standardize SKU values and column names early in your flow for seamless joins and easier downstream data transformation using steps like **Edit columns**, [Extract with AI](https://parabola.io/product/transform/extract-with-ai), and **Standardize with AI**. - Be thoughtful about which inventory columns to compare, considering values like 'Available,' 'On-Hand,' and other relevant fields for your workflow. - Consider incorporating root cause analysis to identify patterns and potential integration issues (e.g., unsynced SKUs or system errors) to minimize future mismatches. Freight and parcel invoice audit ##### What is a freight and parcel invoice audit? [Freight and parcel invoice auditing](https://parabola.io/blog/freight-audit-process) is the process of ingesting invoices from various carriers and auditing them against the carrier’s rate card to identify discrepancies in fuel surcharges, line items, accessorial fees, and other charges. This ensures billing accuracy and prevents overpayments. Freight and parcel invoice audits can be challenging because of the consolidation and standardization required across carrier portals, PDFs, and emails, since every carrier formats their data differently. ##### How does Parabola support freight and parcel invoice audits? With Parabola, you can parse PDF invoices from carriers and ingest invoice data via invoice, match charges with your rate card, calculate invoicing overages, and trigger alerts for discrepancies above a certain threshold. **Want to see the use case in action? **[Check out this video overview](https://youtu.be/8gOpklp2dpE?si=kEp0mcr8ghwBpNAt)**.** ### How to automate a freight and parcel invoice audit ▼ How to build 1. Pull invoice data from sources like PDFs, emails, carrier portals, and/or CSV files using steps like [Pull from inbound email](https://parabola.io/product/integration/email-a-file). 1. Clean and standardize the data using steps like **Edit columns**, [Extract with AI](https://parabola.io/product/transform/extract-with-ai), and [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai) to normalize values and match formatting across your invoices and rate cards. 1. Using a [Combine tables](https://parabola.io/product/transform/combine-tables) step, join the datasets based on identifiers such as shipping method, bill weight, unit of measure, and pricing zone. 1. Calculate discrepancies by applying math formulas to compare the invoice data against the rate card using an [Add math column](https://parabola.io/product/transform/insert-math-column) step, or use the AI-enabled [Custom transform](https://parabola.io/product/transform/custom-transform) step for complex calculations. 1. Action on discrepancies with the [Email a file attachment](https://parabola.io/product/integration/email-attachment) step by setting up email notifications for discrepancies exceeding a defined threshold, such as 3% or a specific dollar amount. 1. Create a dashboard or visualization in Parabola to track discrepancies across carriers and shipping methods. 1. Optionally, use this data in a carrier scorecard Flow to monitor billing accuracy as a performance metric. #### Tips - The simplest way to get started on a freight or parcel audit, before integrating via email or connecting an API, is often with a static file — using a step like [Pull from CSV file](https://parabola.io/product/integration/csv-file) or [Pull from PDF file](https://parabola.io/product/integration/pdf-file). - Standardize carrier names, shipping methods, and accessorials early in your flow using steps like **Edit columns**, [Extract with AI](https://parabola.io/product/transform/extract-with-ai), and **Standardize with AI**. - Consider standardizing and combining rate sheets for a single carrier at the beginning of your flow using a **Stack tables** step to streamline comparisons. - Create visualizations to monitor billing accuracy across carriers and identify discrepancies by shipping service. Tracking inbound freight ##### What is inbound freight tracking? [Tracking inbound freight](https://parabola.io/blog/ops-leaders-on-managing-inbound-freight-amidst-challenging-macro-conditions) — or inbound freight management — is the process of monitoring containers in real time across carriers and freight forwarders within your network. This centralizes shipment data to improve visibility and enable proactive decision-making. The data often comes from various sources, including APIs, carrier portals, emails, and CSV files. ##### How does Parabola support tracking inbound freight? With Parabola, you can pull carrier updates from APIs and email, standardize carrier formats across sources, and consolidate shipment data in a single dashboard for unified tracking. **Want to see the use case in action? **[Check out this video overview](https://youtu.be/bpJBiUXuYfc?si=BItCgyXy7iogcI0B)**.** ### How to automate inbound shipment tracking ▼ How to build 1. Integrate with your carriers and freight forwarders through APIs, carrier portal imports, or email-based data extraction using steps like [Pull from API](https://parabola.io/product/integration/api) and [Extract from email](https://parabola.io/product/integration/extract-from-email). 1. Use AI to extract and standardize data formats from sources like email bodies and CSV files using steps like [Edit columns](https://parabola.io/product/transform/edit-columns) and [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai). 1. Standardize shipment data values such as dates, statuses, and carrier names to create a unified format using steps like [Format dates](https://parabola.io/product/transform/format-dates). 1. Combine all data across sources using the [Stack tables](https://parabola.io/product/transform/stack-tables) step to centralize the information. 1. Action on the data by pushing it to an [ERP](https://parabola.io/parabola-and/erp), Google Sheet, or [Slack notifications](https://parabola.io/product/integration/slack) for team updates, or creating a [shipment tracking dashboard](https://parabola.io/product/integration/visualize) in Parabola. #### Tips - Ensure early standardization of dates, statuses, and carrier names using tools like [Standardize with AI](https://parabola.io/product/transform/standardize-with-ai) and [Format dates](https://parabola.io/product/transform/format-dates). - Use the **Add text column** step to label shipment lines with their data source for clarity (e.g., Flexport or Kuehne + Nagel). - Build modular flows that accommodate updates or changes in carrier data sources. With some building experience under your belt, continue reading for more building resources. --- # Additional data sources Source: https://parabola.io/resources/parabola-university/pulling-data-additional-data-sources Learn about some of the 10,000+ data sources that customers have integrated with Parabola. Interested in more specific lessons? Check out the following bonus lessons: 1. [Pulling data from emails ](https://parabola.io/resources/parabola-university/pulling-from-emails) 1. [Pulling data from PDFs ](https://parabola.io/resources/parabola-university/pulling-from-pdfs) ### Building challenge This lesson doesn’t have a challenge – continue onto the next lesson to continue the course. --- # Pulling data: key concepts Source: https://parabola.io/resources/parabola-university/key-concepts-pulling-data In this first lesson, we’ll dive into one of the essential first steps of Parabola: importing your data. ### Building challenge To get started, we’ll import CSV data into our Flow by following these steps: 1. If you haven’t already, click [this link](https://parabola.io/api/clipboard/4875bb22-d42c-40f7-9681-466b0e1e7807/copy_to_flow?name=Parabola+University) to create a new Flow 1. Download this sample [sales dataset](https://drive.usercontent.google.com/u/1/uc?id=1Z6T_-eT-FjEmAyLe3E2eKbUL5xzyNHgF&export=download) 1. Add a card (right click) to the canvas titled “Pull sales data” 1. Add a **Pull from CSV file** step to the “Pull sales data” card to load the sales data into Parabola #### [Check your work](https://www.notion.so/parabola/Pulling-data-key-concepts-2a7895a0a0de806a878be9a78f302925?pvs=25) --- # Pulling from emails Source: https://parabola.io/resources/parabola-university/pulling-from-emails One of the most common methods for pulling data into Parabola is via email. This method is incredibly useful when working with third-party vendors, carriers, and other partners who send data in formats that don’t map directly to your internal structure and also when working with systems that support sending out automated email reports. This step supports CSV, PDF, Excel, and JSON files, plus it allows you to pull in email subjects, bodies, file names, and information about the sender. ## How to use the step 1. Drag a **Pull from inbound email** step onto the canvas. Every Flow comes with a dedicated email address: Open the **Pull from inbound email** step to access the address for your Flow. 1. Send an attachment to your dedicated email address. 1. Click the Refresh data button to pull your file into the Flow. Note: It can take up to a minute for your file to Flow into Parabola. ## Pro tip - Once you’re ready to fully automate your process, you can set up an auto-forwarding rule to have emails automatically Flow into Parabola. - Many systems allow you to automatically export a CSV report on a recurring schedule. You can have that auto-exported CSV Flow directly into Parabola. Visit our [support docs](https://parabola.io/integration/email-attachment) to learn more about the step. --- # Pulling from PDFs Source: https://parabola.io/resources/parabola-university/pulling-from-pdfs In this lesson, we tackle one of the more challenging aspects of data automation — turning unstructured PDF data into nice, clean data tables. **Looking for a sample PDF? Click**[here](https://drive.usercontent.google.com/u/1/uc?id=1y8Teft510-EFTjmcomjg165iSVJHYvIh&export=download)**.** ## About Parabola’s PDF steps Parabola isn’t just another PDF parser. Parabola’s approach to PDF parsing is unique for a few reasons: 1. The tool was built to handle variations that exist between document formats. 1. Our **Pull from PDF file** and **Pull from inbound email** steps pull data from documents using a blend of PDF scraping and a computer vision-enabled large language model (LLM). 1. Parsing can be easily set up by simply typing in what you want to extract, and it doesn’t need the documents to be in the exact same format every time. 1. Since it’s connected to the rest of Parabola, you can do additional data transformations beyond just pulling data off of a page. ## How to use the step As you get started working with PDFs in Parabola, here are some core concepts to understand: 1. Pull PDFs from emails or static files:****You can work with PDFs using the [Pull from PDF file](https://parabola.io/integration/pdf-file) and [Pull from inbound email](https://parabola.io/integration/email-attachment) steps. If you’re using the **Pull from PDF file** step, you can set up an auto-forwarding rule to have emails automatically flow into Parabola. 1. Auto-detected Tables:****After scanning the documents with AI, Parabola will auto-detect Tables that exist in the document. Simply select which Table you’d like to extract, and Parabola will automatically show you all of the associated columns. 1. Keys:****Beyond columns that exist in Tables, you often need to pull document-level data points from PDFs as well. Things like IDs and dates which are often found at the top of the page. Simply type in the additional values you need to extract in the Keys section of the settings. ## Pro tip - Whenever you’re working with LLMs, the output will be more effective when you add additional context and examples — and Parabola is no exception. To improve output results, provide example values and additional context for those hard-to-parse values. Visit our [support docs](https://parabola.io/integration/pdf-file) to learn more about working with PDFs in Parabola. --- # Adding an if/else column Source: https://parabola.io/resources/parabola-university/transforming-data-add-if-else-column While if statements can get quite complex and messy in spreadsheets, Parabola makes it really outline the logic involved in your if/else statements. ### Building challenge - To the card titled “Calculate revenue by row and assign statuses”, drop an **Add if/else column** step on the card. - Create an Order Value column with the following statuses:If Revenue is greater than or equal to 100, set the value to “High” - If Revenue is between 50 and 99, set the value to “Medium” - Else, set the value to “Low” #### [Check your work](https://www.notion.so/parabola/Add-if-else-column-2a7895a0a0de80d79053e886d7ac6b45) ‍ --- # Adding a math column Source: https://parabola.io/resources/parabola-university/transforming-data-add-math-column With the **Add math column** step, you can write formulas to calculate numeric values — similar to how you might in a spreadsheet. ### Building challenge - Add a card titled “Calculate revenue by row and assign statuses” after our “Reformat date values” card, and drop an **Add math column** step on the card. - Within the **Add math column** step, create a column called Revenue, which is calculated by doing Line Item Price times Units #### [Check your work](https://www.notion.so/parabola/Add-math-column-2a7895a0a0de805587a6e9b94db1fd06) ‍ --- # Adding a text column Source: https://parabola.io/resources/parabola-university/transforming-data-add-text-column-2 Parabola’s **Add text column** step allows you to insert new columns into your datasets. ## How to use the step 1. Add an **Add text column** step to the canvas. 1. Name the new column you want to create. 1. Specify the value you want in the new text column. ## Pro tip - To reference values from other columns, use curly brackets ({}) in the Column value section. Ex) If you have a column called SKU with a value 12345 and you want your new column called Updated SKU to display #12345, you would input “#{SKU}” in the Column value section. Visit our [support docs](https://parabola.io/transform/insert-text-column) to learn more about the step. --- # Combining tables Source: https://parabola.io/resources/parabola-university/transforming-data-combine-tables The **Combine tables** step allows you to join together two tables based on one or more unique identifiers. This step is Parabola’s equivalent of Excel’s “VLOOKUP” and “INDEX MATCH” — without the tricky syntax. ## How to use the step 1. Drag a **Combine tables** step onto the canvas. 1. Specify rules for keeping rows from inputs 1 and 2. - The most common approach is to keep all rows from one table, and find only matching rows from another table. - Check out the visual below for an overview of the four different types of join options. 1. Tell Parabola which column(s) to use for the join. - This will often be something like a unique ID, email address, SKU, etc. 1. If you are joining on multiple columns (like SKU and Warehouse), click the + add a match button and specify the criteria for your additional join(s). - Make sure to specify whether this is an Any match or an All match. ## Pro tip - The input step that you connect first will always be Input 1. - Keep an eye on the number of rows in your input datasets and Results view. If you have more rows than expected in your Results view, you likely need to edit the settings in the step. - Understanding the concepts behind joining data is the hardest part. Remember: There’s no mistake that you can make in Parabola that you can’t just undo. To learn more, check out our [support docs](https://parabola.io/transform/combine-tables). --- # Counting by group Source: https://parabola.io/resources/parabola-university/transforming-data-count-by-group The **Count by group** step allows you to quickly summarize data by counting occurrences within your dataset. It’s particularly useful for flagging duplicate values that exist in a dataset. ## How to use the step 1. Drag a **Count by group** step onto the canvas. 1. Select the column(s) that you want to count unique values within. - Ex) If you want to count up how many orders were placed each month, you would just select the Month column. If you want to count up how many orders were placed each month *by state,*****you would select *both* Month and State. ## Pro tip - If you want to alert the team of duplicates in your dataset, after the **Count by group** step, use the **Filter rows** and **Email a CSV attachment** steps to notify the team about duplicate values. To learn more, check out our [support docs](https://parabola.io/transform/count-by-group). --- # Custom transform Source: https://parabola.io/resources/parabola-university/transforming-data-custom-transform Learn about the most flexible step in Parabola—the Custom transform step. ### Curious to learn more? Check out our [bonus lesson on prompting best practices](https://parabola.io/resources/parabola-university/ai-fundamentals-prompting-best-practices) and [sample prompts](https://parabola.io/product/transform/custom-transform#sample-prompts). ### Building challenge *Note: This lesson was added after the course's initial release, so you won't see this step in subsequent screenshots.* - Beneath the **Add math column** step on the "Calculate revenue by row and assign statuses" card, add a **Custom transform**step. - In the instructions, paste: *Calculate a column called "Revenue" by multiplying "Line Item Price" times "Units". Format the value as currency with 2 decimal places (ex. $1,000.00).*‍ - Once the step processes, click, "Optimize this prompt". #### [Check your work](https://www.notion.so/parabola/Custom-transform-2a7895a0a0de80229fe8d2fba308e68c) ‍ ‍ --- # Editing columns Source: https://parabola.io/resources/parabola-university/transforming-data-edit-columns In this lesson, we dive into one of the most foundational and frequently used steps in Parabola: the **Edit columns** step. ### Building challenge Let’s put this step in action using the sample data downloaded in the “Pulling data | key concepts” lesson: Keep the following columns and put them in the following order: - Order Number - Product Title - SKU# - Status - Order Date - Line Item Price - Units - Rename SKU# to SKU. #### [Check your work](https://www.notion.so/parabola/Editing-columns-2a7895a0a0de8079a11dceae89ead3e8) ‍ --- # Filtering rows Source: https://parabola.io/resources/parabola-university/transforming-data-filter-rows The **Filter rows** step allows you to cut down your dataset by keeping or removing rows based on specific conditions. ### Building challenge - Remove any rows from the sales file where units are equal to zero, or the status is equal to Cancelled or Refunded. #### [Check your work](https://www.notion.so/parabola/Filtering-rows-2a7895a0a0de80ec86bed522cd74512c) ‍ --- # Formatting dates Source: https://parabola.io/resources/parabola-university/transforming-data-format-dates Parabola’s **Format dates** step converts date and time values from any format into a standardized format that fits your needs. ### Building challenge - Add a card titled “Reformat date values” directly to the right of your “Pull sales data” card. - To the card, add a **Format dates** step and reformat the Order Date column from *MMM/dd/yy HH:mm:ss* to the *MM yyyy* format. #### [Check your work](https://www.notion.so/parabola/Formatting-dates-2a7895a0a0de805c9777f9c6bd5c44b5) ‍ --- # Formating numbers Source: https://parabola.io/resources/parabola-university/transforming-data-format-numbers When working with partners and external systems, it’s common to receive data that doesn’t quite match the format you require. The **Format numbers** step transforms values into whatever custom numeric value you require. ## How to use the step 1. Drag a **Format numbers** step onto the canvas. 1. Select the columns you want to reformat. 1. Specify an updated format for the column values. 1. Click the three dots next to your format selection to access advanced settings such as number of decimal places, currency symbols, and rounding options. ## Pro tip - Unlike traditional spreadsheets that require data types to be specified per column, Parabola handles this automatically. - If you ever run into an Invalid Format issue while using an **Add math column**step (which we’ll go over in the next lesson), use the **Format numbers** step before the calculation to ensure the data is properly formatted. Visit our [support docs](https://parabola.io/transform/format-numbers) to learn more about the step. --- # Transforming data: key concepts Source: https://parabola.io/resources/parabola-university/key-concepts-transforming-data In this lesson, we explore the core of what makes Parabola so powerful: its data transformation capabilities. ### Pro tip: Start by cleaning your data The best way to start your Flow is by ensuring your data is as clean as possible at the beginning. Think about doing things like: - Removing unnecessary columns - Removing duplicates - Clearly naming your columns - Filtering out unnecessary columns - Formatting dates ### Building challenge This lesson doesn’t have a challenge – continue onto the next lesson to continue the course. --- # Removing duplicates Source: https://parabola.io/resources/parabola-university/transforming-data-remove-duplicates Using the **Remove duplicates** step, you can ensure your datasets are always free of duplicate values. ## How to use the step 1. Drag a **Remove duplicates** step onto the canvas. 1. Specify which column(s) you want to check for duplicates within. ## Pro tip - In spreadsheets, to dedupe based on values in multiple columns, you might use a concatenate function to combine values from multiple columns. There’s no need to do this in Parabola — you can just select multiple columns, and Parabola will do the rest. Visit our [support docs](https://parabola.io/transform/remove-duplicate-rows) to learn more about the step. --- # Suming by group Source: https://parabola.io/resources/parabola-university/transforming-data-sum-by-group If you’ve ever used spreadsheets to create pivot Tables for data analysis, you’ll love Parabola’s grouping steps. These steps simplify the process of aggregating data, allowing you to summarize complex datasets in just a few clicks. ### How to use the step 1. Drag a **Sum by group**step onto the canvas 1. **Sum:**Select columns with numeric values to sum up. - Ex) Revenue and Units Sold 1. **Group by:**Select which columns you’d like to group your aggregations based on. - Ex) Month and SKU To learn more, check out our [support docs](https://parabola.io/transform/sum-by-group). --- # Transforming with AI Source: https://parabola.io/resources/parabola-university/transforming-data-with-ai Learn about Parabola’s AI transformation steps designed to categorize, standardize, and extract information from large datasets. ### Building challenge To the right of the card titled “Calculate revenue by row and assign statuses”, add a new card titled “Categorize products with AI” and add a **Categorize with AI** step to the card. Assign each value in the Product Title column into one of the following categories: - Pants - Tops - Hats - Other #### [Check your work](https://www.notion.so/parabola/Transform-with-AI-2a7895a0a0de80e69362e3d995439f0d) ‍ --- # AI Vs Manual Methods For Data Enrichment Source: https://parabola.io/questions/ai-vs-manual-methods-for-data-enrichment Data enrichment is vital for building accurate customer and vendor records, but manual research is slow and inconsistent. Traditional approaches often involve spreadsheets and endless lookups. With Parabola, AI‑powered enrichment automatically validates, fills gaps, and refreshes datasets at scale. --- # AI Vs Manual Methods For Data Standardization Source: https://parabola.io/questions/ai-vs-manual-methods-for-data-standardization Compare AI vs manual data standardization — uncover how automation improves accuracy, consistency, and speed. --- # AI Vs Manual Methods For OCR Invoice Processing Source: https://parabola.io/questions/ai-vs-manual-methods-for-ocr-invoice-processing Compare AI vs manual OCR invoice processing and discover how AI automation saves time, reduces errors, and scales seamlessly. --- # AI Vs Manual Methods For Workflow Automation Source: https://parabola.io/questions/ai-vs-manual-methods-for-workflow-automation Compare AI vs manual workflow automation — learn how AI eliminates errors, speeds execution, and scales with your business. --- # Benefits Of AI In PDF Data Extraction Source: https://parabola.io/questions/benefits-of-ai-in-pdf-data-extraction Discover the benefits of AI in PDF data extraction — accuracy, scalability, and speed without manual effort. --- # Best AI Tools For Data Standardization Source: https://parabola.io/questions/best-ai-tools-for-data-standardization Best AI data standardization tools: ensure clean, consistent, and scalable data for reporting and automation. --- # Best AI Tools For Document Digitization Source: https://parabola.io/questions/best-ai-tools-for-document-digitization Best AI document digitization tools: convert paper files into reliable, structured, and automated digital data. --- # Best AI Tools For PDF Data Extraction Source: https://parabola.io/questions/best-ai-tools-for-pdf-data-extraction Best AI PDF extraction tools: convert static files into reliable, structured data without manual entry. --- # Best Practices For Inventory Reconciliation Source: https://parabola.io/questions/best-practices-for-inventory-reconciliation Best practices for inventory reconciliation: improve accuracy, visibility, and control over your stock data. --- # Best Practices For Order Consolidation Source: https://parabola.io/questions/best-practices-for-order-consolidation Best practices for order consolidation: save on shipping and improve fulfillment efficiency. --- # Best Software For Freight Invoice Audits Source: https://parabola.io/questions/best-software-for-freight-audits Find the best freight audit software: re-rate invoices, flag errors automatically, and recover shipping overcharges. --- # Best Tools For Purchase Order Management Source: https://parabola.io/questions/best-tools-for-purchase-order-management-automation Best tools for PO automation: streamline approvals, match invoices automatically, and keep suppliers aligned. --- # Best Workflow Automation Softwares Source: https://parabola.io/questions/best-workflow-automation-softwares Best workflow automation software: eliminate manual work, connect systems, and scale business operations. --- # Can I Convert a PDF to an Excel Spreadsheet Source: https://parabola.io/questions/can-i-convert-a-pdf-to-an-excel-spreadsheet Learn how to convert a PDF into an Excel spreadsheet, common challenges in extracting tabular data, and how Parabola automates PDF-to-Excel conversion at scale. --- # How Can I Get Data From a PDF Into Excel Source: https://parabola.io/questions/how-can-i-get-data-from-a-pdf-into-excel See how to extract and structure data from a PDF into Excel, and learn how Parabola automates the process for invoices, manifests, and reports. --- # How To Automate Bill Of Materials Reconciliation Source: https://parabola.io/questions/how-to-automate-bill-of-materials-reconciliation Automate BOM reconciliation to prevent delays, reduce write‑offs, and maintain a single source of truth for operations. --- # How To Automate Purchase Order Management Source: https://parabola.io/questions/how-to-automate-purchase-order-management Automate PO management to streamline approvals, reduce errors, and keep suppliers aligned with finance. --- # How To Automate Returns Management Source: https://parabola.io/questions/how-to-automate-returns-management Automate returns to improve customer experience, restock faster, and reduce refund delays. --- # How To Automate Your Freight Audits Source: https://parabola.io/questions/how-to-automate-freight-audit Automate freight audits to recover overcharges, reduce disputes, and speed up invoice reconciliation. --- # How To Automate Your Parcel Invoice Audits Source: https://parabola.io/questions/how-to-automate-parcel-invoice-audit Reduce parcel spend and improve cash flow by automating invoice audits across carriers. --- # How To Build Reports Using Data From Netsuite And Slack Source: https://parabola.io/questions/how-to-build-reports-using-data-from-netsuite-and-slack Automate NetSuite reporting into Slack to keep teams updated with accurate, real‑time insights. --- # How To Build Reports Using Data From Netsuite And Snowflake Source: https://parabola.io/questions/how-to-build-reports-using-data-from-netsuite-and-snowflake Automate NetSuite and Snowflake reporting to unify data, reduce manual work, and keep insights always up to date. --- # How To Connect Netsuite To Hubspot Source: https://parabola.io/questions/how-to-connect-netsuite-to-hubspot Connect NetSuite and HubSpot to keep sales, marketing, and finance aligned with accurate, real‑time data. --- # How To Connect Netsuite To Snowflake Source: https://parabola.io/questions/how-to-connect-netsuite-to-snowflake Automate connecting NetSuite to Snowflake to keep analytics fresh and reduce manual data prep. --- # How To Connect Shopify To Amazon Seller Central Source: https://parabola.io/questions/how-to-connect-shopify-to-amazon-seller-central Automate Shopify and Amazon Seller Central integration to keep inventory, listings, and orders in sync without manual updates. --- # How to Digitize a PDF Source: https://parabola.io/questions/how-to-digitize-a-pdf Learn how to digitize a PDF, the difference between scanning and digitizing, and how Parabola automates PDF digitization for structured, searchable data. --- # How to Find Out My HTS Code Source: https://parabola.io/questions/how-to-find-out-my-hts-code Learn how to find your product’s HTS code, what resources to use for classification, and how Parabola automates HTS lookup and compliance workflows. --- # How to Map GL Account Source: https://parabola.io/questions/how-to-map-gl-account Learn how to map GL accounts across systems, why it’s critical for financial accuracy, and how Parabola automates GL account mapping at scale. --- # How to Reconcile Physical Inventory Items Source: https://parabola.io/questions/how-to-reconcile-physical-inventory-items Learn how to reconcile physical inventory with system data step by step. Understand common challenges, best practices, and how Parabola automates inventory reconciliation across systems. --- # Is It Possible to Parse a PDF File Source: https://parabola.io/questions/is-it-possible-to-parse-a-pdf-file Learn what it means to parse a PDF file, how parsing differs from simple conversion, and how Parabola automates PDF parsing for structured data extraction. --- # Best Practices For Returns Management Source: https://parabola.io/questions/best-practices-for-returns-management Best practices for returns management: reduce costs, improve accuracy, and enhance customer satisfaction. --- # Steps To Implement Bill Of Materials Reconciliation Source: https://parabola.io/questions/steps-to-implement-bill-of-materials-reconciliation Learn the key steps to implement BOM reconciliation and reduce costly errors with automated workflows. --- # Steps To Implement Inventory Reconciliation Source: https://parabola.io/questions/steps-to-implement-inventory-reconciliation Follow these steps to implement inventory reconciliation and prevent costly stock errors. --- # Steps To Implement Purchase Order Management Source: https://parabola.io/questions/steps-to-implement-purchase-order-management Discover the steps to implement purchase order management and improve control over spend and suppliers. --- # What AI is best for operations management? Source: https://parabola.io/questions/what-ai-is-best-for-operations-management Learn which AI tools are best for operations management, including automation, forecasting, and decision-support platforms that help operators scale efficiently. --- # What Are the 5 Steps of Inventory Management Source: https://parabola.io/questions/what-are-the-5-steps-of-inventory-management Learn the five key steps of inventory management and how Parabola helps automate tracking, replenishment, and reconciliation across systems. --- # What Is a Carrier Scorecard? Source: https://parabola.io/questions/what-is-a-carrier-scorecard Learn what a carrier scorecard is, what metrics it includes, and how operators use automation with Parabola to maintain performance visibility across carriers. --- # What Is a Parcel Audit Source: https://parabola.io/questions/what-is-a-parcel-audit Learn what a parcel audit is, how it helps uncover carrier overcharges, and how Parabola automates the auditing of parcel invoices at scale. --- # What Is a Scorecard in Logistics Source: https://parabola.io/questions/what-is-a-scorecard-in-logistics Discover what a logistics scorecard is, which KPIs matter most, and how to automate logistics scorecard reporting using Parabola. --- # What Is Automation in Inventory Management Source: https://parabola.io/questions/what-is-automation-in-inventory-management Discover what inventory automation is, how it helps reduce errors and manual work, and how Parabola simplifies automation for operations teams. --- # What Is GL Account Mapping Source: https://parabola.io/questions/what-is-gl-account-mapping Learn what GL account mapping is, why it’s essential for accurate financial reporting, and how Parabola automates it across systems like SAP and NetSuite. --- # What Is Mapping in Accounting Source: https://parabola.io/questions/what-is-mapping-in-accounting Understand what mapping means in accounting, how it connects subledger data to your general ledger, and how Parabola automates account mapping and reconciliation. --- # What Is No Code Automation? Source: https://parabola.io/questions/what-is-no-code-automation A beginner-friendly guide to building workflows without engineering support. --- # What Is the 80/20 Rule in Inventory Management Source: https://parabola.io/questions/what-is-the-80-20-rule-in-inventory-management Learn how the 80/20 rule shapes modern inventory management. Understand how to identify high-impact SKUs and automate prioritization using Parabola. --- # What Is the Freight Invoice Audit Process Source: https://parabola.io/questions/what-is-the-freight-invoice-audit-process Understand the freight invoice audit process, the steps involved in verifying carrier charges, and how Parabola automates freight audit workflows for logistics teams. --- # What Is the HTS Code for a Product Source: https://parabola.io/questions/what-is-the-hts-code-for-a-product Understand what an HTS code is, why it’s assigned to every imported product, and how Parabola automates product classification for customs and compliance. --- # What Is the Inventory Reconciliation Process Source: https://parabola.io/questions/what-is-the-inventory-reconciliation-process Understand the reconciliation process, why it’s essential for supply chain accuracy, and how Parabola automates it across systems. ---