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Supplier Operational Capacity, Not Competitors, Is Capping Growth 

When a supplier stalls out on growth, the instinct is to look outward, like a competitor undercutting a bid or a retailer shifting its buy. But for most suppliers and brands, the real ceiling sits closer to home: systems and processes built for a smaller version of the business that are still running the show after the business outgrew them. 

Order volume outpaces systems more often than it outpaces demand.

 Retailers keep adding requirements and order volume keeps climbing. But the supplier's internal processing, onboarding steps, order execution workflow, and exposure to chargebacks stays sized for last year's business.  

Retailers rarely stop buying. The operation underneath just can't absorb what's being asked of it. 

Why Do Suppliers Hit a Growth Ceiling Before They Hit a Demand Ceiling?

Retailers rarely turn away a supplier who can perform. What they do turn away is risk: missed ship windows, inconsistent data, and compliance gaps that turn into fines. A supplier whose operations lag behind its order volume looks, from the retailer's side, exactly like a supplier who can't handle more business, even when demand is there and growing. 

That distinction matters because it changes where a supplier should be looking for the fix. Adding sales headcount or chasing new retail relationships doesn't help if the order execution behind the scenes is already stretched. The fix lives in operations, not in the sales pipeline. 

Three operational limits show up again and again in supplier growth stories: how fast a supplier can onboard new retailers, how well its order execution holds up under volume, and how exposed it is to chargebacks along the way. 

Supplier Onboarding Speed With New Retailers

Every new retailer relationship comes with its own requirements: labeling standards, shipping windows, invoicing rules, electronic data interchange (EDI) formats, and more. When onboarding runs on manual setup and one-off spreadsheets, each new relationship takes weeks the supplier doesn't have. A retailer ready to place a first order doesn't wait for a supplier to catch up. 

Order Execution Capacity for Scaling Order Volume

Order execution is where a growing order count meets a process built for a smaller one. Purchase orders that once flowed through one or two channels now arrive through direct-to-store, distribution center, dropship, and marketplace paths at once. Without a consolidated way to see and act on all of it, execution slows exactly when speed matters most. 

, or retailer-issued deductions for compliance misses like late shipments, mislabeled cartons, or short orders, scale with volume, not intent. A supplier can be executing in good faith and still see chargebacks climb simply because there are more orders, more retailers, and more chances for a data or timing gap to slip through. Left unmanaged, that exposure eats the margin gained from the additional volume. 

How Does Chargeback Exposure Compound the Capacity Problem?

Chargeback exposure costs more than money; it eats the time teams need to prepare for what's next. Supplier teams that spend their week disputing deductions and chasing down documentation have less time to prepare for the next retailer or the next volume increase. The operational drag compounds: More orders create more chargeback risk, more chargeback risk consumes the staff time needed to scale onboarding and execution, and the growth ceiling gets lower instead of higher. 

This is why treating chargebacks as a finance team cleanup problem understates the issue. Chargeback exposure is a growth constraint as much as a revenue one. 

What Happens When Suppliers Fix Capacity Instead of Chasing More Demand?

 surveyed 170 suppliers on what changed after they addressed operational capacity directly, rather than treating growth as a demand problem alone. The results point to execution, not headcount, as the lever that moved: 

The pattern across all four findings is the same.

 Suppliers didn't add staff to keep pace. They ran the onboarding, order execution, and chargeback-management work on infrastructure sized for the volume they already had, rather than the volume they had when the process was first set up. 

For a supplier or brand leader whose order volume already outpaces their systems, that's the case for network-powered execution over adding more people to a process that wasn't built to scale. The retail supplier network already understands onboarding requirements, order formats, and compliance rules across thousands of prior relationships, so a new connection doesn't start from zero, and execution doesn't slow down every time volume goes up. 

What does "operational capacity" mean for a supplier?

Operational capacity is how much order volume, how many retailers, and how much compliance complexity a supplier's onboarding, order execution, and deduction-management processes can absorb without breaking down. It's distinct from demand. A supplier can have plenty of demand and still be capped by capacity if the underlying processes can't keep up. 

How can a supplier tell if operations, not demand, are capping growth?

Common signals include onboarding new retailers taking weeks or months instead of days, order execution slowing as volume increases across channels, and chargebacks climbing in step with order count rather than with actual compliance misses. If demand is present but growth still lags, the constraint is usually operational. 

What's the fastest way to expand operational capacity without adding headcount?

Suppliers that scale capacity without adding headcount typically standardize onboarding so it doesn't start from scratch with each new retailer, consolidate order execution across channels onto one operating view, and manage chargeback exposure proactively instead of disputing deductions after the fact. 

See What Suppliers Gained by Fixing Capacity, Not Headcount

 and the independently conducted TEI Impact Study both point to the same conclusion: Suppliers that address operational capacity directly see faster onboarding, steadier execution, and stronger confidence in their ability to grow. Read the 

Not ready for that conversation yet? The Supply Chain Source has more on scaling supplier operations at 

Why Demand Forecasting Accuracy Starts Before the Forecast Runs 

What's Actually Causing Poor Forecast Accuracy in Retail? 

The instinct is to blame the model. A retailer's forecast misses, and the response is almost always the same: Buy a better algorithm, hire another data scientist, add more external signals. Meanwhile the miss rate barely moves. 

Here's the harder truth. Inventory distortion, the combined cost of stockouts and overstocks, cost the global retail industry $1.73 trillion in 2025, according to 

. Supply chain disruption was the single largest contributor, responsible for $301 billion of that figure on its own. This is a structural problem, and it sits upstream of where most retailers are looking. 

The forecast is only as good as what feeds it. If a supplier confirms a purchase order (PO) late, ships early or short, or sends an inaccurate advance shipping notice (ASN), the retailer's system of record is wrong before the forecast model ever touches the data. No amount of algorithmic sophistication fixes a training set that was corrupted at the source. 

How Does One Late ASN Turn Into a Bad Forecast? 

A supplier ships an order without sending an accurate ASN, or sends one with the wrong item count. The receiving team at the distribution center can't match what physically arrived against what the system expected, so the discrepancy sits unresolved. That unresolved discrepancy distorts the perpetual inventory count. Now the system thinks it has more (or less) of an item than it actually does. 

From there, two things happen, and neither is good. Either the system shows a phantom in-stock position while the shelf is empty, or it triggers unnecessary safety stock and expedited freight to cover a shortage that doesn't exist. Either way, the demand signal for that SKU is now wrong. And that wrong signal becomes training data for next month's forecast. 

Multiply that across a supplier base of any real size. Every late confirmation, every receiving discrepancy, every unconfirmed shipment stacks on the last. By the time next month's forecast runs, it was never working with real numbers to begin with. 

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Target Canada's 2015 collapse is the most visible example of this failure mode playing out at scale, even if the technology has moved on since. Target Canada launched with a new inventory system that the internal teams didn't fully understand, and no historical sales data to check it against. Product data accuracy across roughly 75,000 items was compromised from the start, with dimensions, units, and currencies entered inconsistently by newly hired staff working against an unrealistic timeline. 

The forecasting system had nothing reliable to learn from. Distribution centers overflowed with product that had nowhere to go, while store shelves sat empty. Reporting on the collapse later found that some inventory analysts had even disabled auto-replenishment alerts to keep their in-stock percentages looking acceptable on paper, which only masked the problem further. The forecast wasn't the failure. The data feeding it was broken long before any model ran. 

Doesn't AI-Driven Forecasting Fix This? 

It's a fair question, and the honest answer is that AI-driven forecasting makes the execution-data problem more urgent, not less. 

McKinsey's research on demand-sensing and machine learning forecasting found that companies using these methods achieved roughly 90% forecast accuracy with a three-month lag, compared with around 60% for manual forecasting. That's a real gain. But it depends entirely on feeding the model clean, connected external data, things like point-of-sale signals and confirmed stock-out events. A supplier data vacuum starves that exact input. An AI model inherits whatever corruption is already in the pipeline, and it inherits it faster and at greater scale than a human planner would have. 

Nike's experience with its i2 demand-planning system in 2000 makes the same point from a different angle. The $400 million implementation produced forecasts so far off that Nike overbought roughly $90 million of slow-selling shoes while shorting popular models by $80 million to $100 million. Part of the failure was that the system couldn't communicate with Nike's existing data sources, and much of the underlying data was still entered by hand. A sophisticated model, fed on broken inputs, still produces broken output. That lesson has aged well into the AI era. 

What Does Fixing Execution at the Source Actually Return? 

This is where the case stops being theoretical. 

, commissioned by SPS Commerce and published in January 2026, interviewed 10 decision-makers across seven retail, grocery, and distribution organizations. For a composite organization with $3.5 billion in annual revenue and more than 1,000 vendors, Forrester found: 

The stockout number gets the attention, but the mechanism behind it is what makes it repeatable. Interviewees described gaining earlier visibility into order fulfillment issues, using vendor performance data to hold suppliers accountable for on-time, in-full (OTIF) delivery, and catching inventory gaps before they became empty shelves. One interviewee, a senior manager of supplier operations at an industrial distributor, said the improved visibility helped their organization fine-tune ordering patterns and demand planning, pushing their annual OTIF performance across vendors to nearly 85%. 

The productivity and receiving numbers aren't side benefits. They're the mechanism. Better PO management and cleaner receiving data are what stop the corruption chain before it reaches the forecast. 

Retailers Already Know This, Even Without Calling It a Data Problem 

Walmart's OTIF program is the clearest evidence that the industry has already reached this conclusion, even if it rarely gets framed as a forecasting fix. Walmart tightened its OTIF standard to 98% in September 2020, explicitly tying the change to keeping shelves stocked and improving product availability for customers. The requirement proved difficult enough in practice that Walmart recalibrated in 2024 to 90% on-time and 95% in-full for prepaid suppliers. 

Whatever the exact threshold, the logic is the same one this article has been building toward: in-stock performance depends on supplier execution discipline, not just on how good the buyer's forecasting tool is. Walmart built an entire compliance program around that premise. Most retailers just haven't connected it back to their own forecast-accuracy complaints yet. Retailers who've formalized this connection, through 

, have seen real revenue gains: Research on OTIF improvements shows that even a five-point gain can generate tens of millions of dollars for a mid-size retailer. 

Is Your Supplier Data Trustworthy Enough for Any Forecasting Tool to Work? 

That's the real question, and it's a different one than "which forecasting tool should we evaluate next." A new model, however capable, still runs on the same execution data your current one does. If that data is fragmented, late, or unconfirmed, the new tool inherits the old problem. 

SPS Commerce doesn't run the forecast. What the SPS network does is align and connect the execution data, POs, ASNs, receiving confirmations, and vendor performance history, that any forecasting system depends on to be trustworthy in the first place. Forrester's independent analysis quantified what fixing that layer is worth: a 5% stockout reduction, $15 million in incremental revenue over three years, and a 360% ROI with payback in under six months. 

, including the assumptions and sensitivity analysis, and share it with your finance team. 

For a closer look at how execution gaps specifically undercut AI-driven buying decisions, see

Why do retail demand forecasts keep missing even after investing in better models?  

Usually because the problem was never the model. Late ASNs, unconfirmed POs, and receiving discrepancies corrupt the data before a forecast ever runs, so a smarter algorithm just processes bad inputs faster. 

What is the connection between supplier execution and forecast accuracy?  

A late or inaccurate ASN distorts receiving data and perpetual inventory counts. Those distortions become the demand history a forecasting model trains on, so accuracy erodes even when the model itself is fine. 

Does AI-driven forecasting solve the supplier data problem?  

Not on its own. McKinsey found AI forecasting can reach roughly 90% accuracy against 60% for manual methods, but only with clean, connected input data. Feed it a fragmented supplier pipeline and it inherits the same corruption, often faster than a human planner would have caught it. 

What financial return comes from fixing supplier execution data?  

 put a number on it: a composite organization saw a 5% stockout reduction worth $15 million in incremental revenue over three years, and a 360% overall ROI. 

Chain Reaction Newsletter Archive

 newsletters archive to explore past editions, with the newest issue listed first. Each edition highlights the latest retail, supply chain, CPG, and AI insights as well as news from 

Welcome to the July edition of Chain Reaction, your source for staying ahead of the news and insights shaping modern supply chains. 

Supply chain volatility is not new. But the pace of disruption in 2026 has made reactive operations genuinely unsustainable. Tariff policy has shifted repeatedly across major trading relationships, port volumes are hitting record highs as brands pull shipments forward to stay ahead of the next change, and the average retail order now changes six times before it ships. The organizations absorbing this without breaking are not the ones with the most sophisticated tools. They are the ones that built clean, connected foundations before the disruption arrived and are now using AI to move faster on top of those foundations. 

The industry's message on volatility is hardening into something practical: move from reactive to predictive, or keep paying the price. 

, supply chain disruptions cost $1.5M per day on average for companies that are not watching until something breaks. The shift procurement teams are making is from periodic audits to real-time risk signal monitoring — catch the exposure early, not after the damage is done. 

Retailers are being pushed in the same direction. 

 shows that agility and resilience are no longer separate goals. The organizations outperforming are the ones acting on shared data quickly, adjusting assortments, and keeping supplier alignment intact rather than chasing problems after they surface. 

Meanwhile, the macro backdrop keeps moving. 

 captures how frequently landed cost assumptions have been upended across Canada, Mexico, China, and the EU. And 

 as retailers and brands front-load shipments to get ahead of the next potential shift. Peak season arrived early this year, whether teams were ready or not. 

From inside hundreds of thousands of trading relationships, the pattern SPS Commerce sees is consistent: the teams navigating this environment with the least disruption are the ones whose data was structured enough to act on when things moved. For CPG brands, that means using AI demand forecasting to model tariff exposure and protect margins before a policy shift forces a reaction. 

 breaks down what separates the brands getting results from those still reacting after the fact: a clean, connected picture of landed cost, sell-through signals, and supplier capacity before the model is asked to reason about risk. 

Volatility is not just a macro story, either. SPS Commerce data shows order changes have doubled year over year, with 1 in 5 orders now coming back with acknowledgement changes. 

, drawn from a session with Sprouts Farmers Market and Sunkist Growers, shows what proactive coordination looks like in practice: weekly data-driven conversations, scenario planning, and onboarding practices that align on item data from day one. For midmarket retailers, promotional stockouts remain one of the most visible and costly places volatility shows up, and the root cause traces back to supplier data accuracy problems that existed weeks before launch, not the demand planning failures that get blamed in post-mortems. 

For teams looking to stay current as conditions keep shifting, 

, the evolution of SupplierWiki, now offers more than 2,800 resources across every part of the trading ecosystem, an AI retail tool built on official industry rules for instant compliance and routing answers, and Expert Exchange, a peer community of 60,000 members where practitioners share what actually works in the field. In a volatile environment, fast access to reliable answers is its own form of resilience. 

This month's newsletter has been all about what it takes to survive volatility — and if this month's theme resonated, we want to know where you actually stand. We're sending a short survey focused on AI readiness and data foundation: the bedrock that determines whether your team can respond to disruption or just absorb it. The questions cover data quality, system connectivity, and where your organization is in its AI journey right now. 

We'll surface the results in August's newsletter, the same way we did with last month's AI usage findings. The more responses we get, the sharper the picture — and the more useful it will be to everyone in the network. Take a few minutes and tell us where things really stand. 

The events and webinars below are ideal places to connect with SPS and continue the conversation with our team. Are you attending any of the below? We'd love to connect.  

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Welcome to the June edition of Chain Reaction, your source for staying ahead of the news and insights shaping modern supply chains. 

Growing your business with AI feels like a no-brainer. AI tools allow us to stay agile, focused, and increase our productivity more than we’ve ever seen. The most important piece, though, is building a solid foundation for your tech stack and ensuring you know what specific agentic tools are built to do, and what they’re not intended for. Ensure your bedrock is solid and set your business up for real growth. 

The industry's message on AI is getting sharper: it doesn't fix broken foundations, it amplifies them. As 

, companies deploying AI on top of duplicate supplier records and siloed inventory data aren't fixing those problems; they're accelerating them. The prescription is simple: fix the foundation first, then layer AI on top. 

The Q1 earnings season showed what that divide looks like in practice. 

 each reported meaningful returns from AI because their investments were built on structured data. Starbucks told the opposite story, 

 because employees had to manually verify every automated count, doubling the work instead of cutting it. 

The race to deploy is accelerating regardless. Amazon is now licensing its Alexa for Shopping technology as an 

, suppliers without clean, connected product data risk going invisible before the sale even starts. 

From inside hundreds of thousands of trading partner relationships, the view is consistent: data quality is where AI investments succeed or stall.

The supply chain teams getting real results from AI aren't starting with the most sophisticated tools. They're starting with clean data. Our guide on 

 shows how these agents work best as high-volume task runners using guardrails. They are effective only when the data underneath them is structured and trustworthy. 

That's especially true when breaking into a new channel. 

 means starting without historical sell-through data, which makes demand planning models unreliable by default. The result, when the foundation isn't there, is a chain of bad decisions: wrong replenishment, missed compliance requirements, and deductions that were entirely preventable. 

 not through heroic effort, but through accurate EDI, automated validation, and visibility from order to payment. Most deductions come from data gaps, not bad intent. 

. When specs, attributes, and compliance docs live in disconnected spreadsheets, no AI tool can reason accurately about that inventory. The answer isn't a better model. It's better data at the source. 

Last month, we asked how AI is actually showing up in your day-to-day work and the results reveal a telling gap. Comfort with AI tools is moderate and clustered tightly: most respondents rated themselves a 6 or 7 out of 10, with no one at either extreme. People aren't intimidated by the technology, but they aren't confident power users yet either. Actual usage, however, tells a more cautious story. The majority of respondents said AI accounts for only 20-30% of their work. Familiarity has arrived. Deep integration hasn't. 

That gap may explain the split on impact. Half of the respondents said AI has made their work easier, a meaningful signal that real value is being realized for those who have put the tools to use. But a third said it hasn't, and another sixth were still unsure. Taken together, the majority of the network is still waiting for AI to deliver on its promise. That's not skepticism, it's an honest reflection of where most organizations are: tools in hand, foundation still being built. 

The through-line from this month's industry conversation is hard to miss. Moderate comfort, limited usage, and uneven results are exactly what you'd expect when AI is layered onto workflows that aren't yet ready for it. The teams reporting real gains are likely the ones who invested in clean data and clear processes first. The rest are still doing the foundational work, which, it turns out, is the right place to start. 

Supply Chain Performance Monitoring: Why 3PLs Are Moving to Real-Time Data 

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Every day, third-party logistics (3PL) providers make decisions that affect transportation costs, service levels, and customer relationships. The challenge is that many of those decisions are still based on information that's already out of date. 

Freight markets shift quickly. Retailer requirements continue to evolve. Fuel prices, labor costs, and customer expectations rarely stay still for long. Waiting for a weekly or quarterly report to understand what went wrong leaves little opportunity to change the outcome. 

The pressure to improve visibility is steadily growing across the industry. Gartner found that only 

 have developed the capabilities they need to remain competitive over the next several years, with leading organizations placing greater emphasis on real-time visibility, integrated ecosystems, and AI-enabled decision-making. 

That is exactly why more 3PLs are moving to real-time supply chain performance monitoring. They’re done looking backwards at completed reports. These days, they are building the visibility needed to identify issues as they emerge to respond faster and make better informed decisions.  

What Is Supply Chain Performance Monitoring? 

Supply chain performance monitoring is the continuous tracking of operational metrics like shipment status, on-time delivery, fuel consumption, driver performance, and asset utilization. Rather than reviewing historical reports after problems have already occurred, operations teams can monitor performance in real time, identify issues earlier, and act while there is still an opportunity to improve the outcome. 

Advances in AI, automation, and connected supply chain data have made this level of visibility practical for organizations of every size. Information that once lived across disconnected spreadsheets and systems can now be shared across transportation, warehouse, and trading partner networks in near real time. Instead of relying on fragmented data or delayed reporting, teams gain a shared view of operations that help them make faster decisions, respond more quickly to disruptions, and continuously improve performance. 

Why Does Real-Time Supply Chain Data Matter for 3PLs? 

Traditional reporting explains what happened. Real-time monitoring helps operations teams influence what happens next. 

When performance data updates continuously, transportation managers can respond to delays, rising costs, or service issues while shipments are still moving instead of discovering problems after customers have already been affected. 

Real-time supply chain performance monitoring dashboard showing KPIs for fuel consumption, driver performance, on-time shipments, asset utilization, trends, and alerts.

3PL Performance Monitoring.png

Continuous monitoring gives operations teams an accurate view of what's happening across their network. Instead of relying on delayed reports or manual updates, planners can adjust routes, rebalance resources, and respond to disruptions using current operational data. 

The same visibility also improves customer communication by making shipment updates easier to share throughout the delivery process. 

Retailer, carrier, and regulatory requirements continue to evolve. Performance monitoring helps organizations identify missing documentation, tracking issues, and reporting gaps before they delay shipments or create compliance challenges. 

Automating much of this reporting also reduces manual work, allowing teams to spend more time improving operations instead of compiling reports. 

Better Cost Control Through KPI Tracking 

Transportation costs can change quickly. Monitoring KPIs such as fuel consumption, driver performance, on-time delivery, and asset utilization helps 3PLs identify inefficiencies while corrective action is still possible. 

Instead of explaining higher operating costs after the fact, operations teams can address them as they develop, protect margins and improve overall performance. 

Retailers, carriers, suppliers, and customers all depend on timely information. When every partner is working from the same operational data, there is less confusion around shipment status, fewer missed handoffs, and faster resolution when plans change. 

Shared visibility also strengthens customer relationships by improving transparency throughout the supply chain. 

Greater Flexibility as the Business Grows 

As customer expectations continue to rise, responsiveness has become part of the service organizations provide. 3PLs that can identify issues early, communicate proactively, and adapt quickly are better positioned to retain existing customers and win new business. 

Because real-time performance monitoring scales alongside operations, it becomes more valuable as organizations add customers, facilities, transportation partners, and distribution channels. 

How Can 3PLs Start Building a Performance Monitoring Practice? 

Most organizations find that monitoring everything is overwhelming and ultimately ineffective. 

Successful performance monitoring initiatives typically start with a small group of business-critical metrics, such as on-time delivery, transportation costs, or asset utilization. Once those measures are consistently visible across the organization, teams expand monitoring into additional systems, workflows, and trading partner relationships. 

The biggest challenge is often connecting data that already exists across transportation management systems (TMS), warehouse management systems (WMS), 

Frequently Asked Questions About Performance Monitoring 

What KPIs should a 3PL monitor in real time? 

Common starting points include on-time delivery, shipment status, fuel consumption, driver performance, asset utilization, transportation costs, and order accuracy. These metrics provide early insight into operational efficiency, customer service, and overall profitability. 

Does real-time monitoring replace quarterly reporting? 

No. Real-time monitoring and historical reporting serve different purposes. Continuous monitoring helps teams respond to issues as they occur, while monthly and quarterly reporting supports long-term planning, budgeting, and performance analysis. 

How long does it take to implement performance monitoring? 

Implementation timelines will depend on the number of systems, trading partners, and data sources involved. Organizations using an established supply chain network can often connect new partners much faster than those building integrations individually. 

See How SPS for 3PLs Delivers Real-Time Visibility 

Everything this article covers, from on-time delivery to asset utilization, depends on connected, standardized data. SPS for 3PLs helps warehouse and transportation providers automate data exchange across every customer, channel, and fulfillment model. Replacing fragmented reporting with visibility teams can act on as conditions change. 

 to see how a standardized, automated foundation supports the real-time performance monitoring your operations need. 

Getting Started in Vendor Central: What 1P Suppliers Need To Know 

Receiving the coveted Amazon Vendor Central invitation is an exciting prospect. After all, in 2025 Amazon accounted for roughly 

 by reaching about $440 billion in sales, and what brand wouldn’t want a piece of that online shopping pie? 

, your hub for managing Amazon business, for the first time and seeing the homepage staring back at you, excitement may melt away into anxiety. Getting your brand set up correctly in Vendor Central is fundamental to launching and scaling your business on Amazon. But without a roadmap, it can be difficult to know where to begin and how to get off on the right foot.  

Amazon Vendor Central Home Page Overview 

When you log into the Vendor Central home page, you will see five different widgets: 

Next, you can move your attention to the menu icon in the upper left corner. If you select it, you will see a list of seven different categories that play an important role in starting and scaling your Amazon business. 

 tab is where you will manage your brand’s product catalog. You can add products (one at a time or in bulk), edit product details, upload images and videos, and manage product compliance.  

 tab is your destination for managing POs, scheduling transportation pickups, sending ASNs, 

 tab gives you options for enhancing product listings and setting up vendor-powered coupons (VPCs), promotions, shopping guide placements, and marketing packages. You can also run A/B tests of alternate content versions to learn more about traffic, engagement, and what converts.  

 tab as your one-stop shop for growing your business. You’ll find recommendations on what to list, where to list, and how to speed up sales.  

 tab offers important business analytics and performance data you can use to track sales, 

, and supply chain issues. You can also use it to monitor operational requirement adherence and to make marketing, forecasting, and other planning decisions. 

Here’s some of the data that will be at your fingertips:

tab is where you will manage all things financial, including: 

tab is where you’ll find educational videos, courses, and webinars designed to help you succeed at Amazon. The 

 will list trainings that are most applicable to where you are in your business, but you can also peruse trainings from these main categories: 

If you are new to Vendor Central, Amazon recommends beginning with the 

If questions arise, you have three main options for getting help. 

Partner With SPS Commerce for Amazon Vendor Central Success 

You don’t have to navigate Amazon Vendor Central alone. Here’s how 

Recall Readiness for Health and Beauty Brands

The majority of plans for product recalls present the wrong question, trying to predict whether a contamination event, a mislabeling error, or a supplier failure will happen in the first place. But the question that actually determines what happens next is narrower and far more urgent: If a recall does happen, can the brand act fast and precisely? 

The moment a recall starts, response time becomes the only variable that matters. The clock starts the instant a safety signal appears, and every hour without an answer is an hour exposure keeps compounding. Everything else waits behind one question: Exactly which lots are affected, and where did they go?  

Recall readiness is not a single document in a compliance folder. Instead, a brand’s data and operations capability determines whether they can trace a lot in hours instead of weeks. 

The Regulatory Floor in Health, Beauty, and Wellness 

For most of the history of the U.S. cosmetics industry, the FDA had almost no lever to pull. The rules, which dated back to 1938, gave the agency authority to act only after a product had already caused harm. There was no federal registration requirement, no mandatory listing of what was in a product, and no way for the FDA to order a company to pull something off shelves. Recalls happened, but they happened because a brand chose to cooperate, not because the FDA could compel it. 

 (MoCRA) was passed, with its core provisions taking effect in December 2023. Due to MoCRA, the FDA can order a mandatory recall of a cosmetic product when it determines the product presents a reasonable probability of causing serious adverse health consequences or death. These are mandatory orders, usually with a public announcement attached. 

 (defined as the manufacturer, packer, or distributor whose name and address appear on a cosmetic product's label) now has to report a serious adverse event to the FDA within 15 business days of learning about it. The FDA requires the responsible parties to keep the underlying records for six years (three years for small-business distributors who don't manufacture or process the product themselves).  

Drugs and supplements have lived with a version of this for years. Cosmetics haven't, which is why this is a bigger shift for health and beauty brands than it might look on paper. And for brands with a portfolio that spans categories, the compliance clock across the whole business now moves at the pace of its most strictly regulated category. 

A 15-business day deadline looks generous on paper but can be difficult for a brand to meet. A brand has to identify the affected lot, trace every location that lot shipped to, and get pull-back moving, all before the reporting deadline. 

For example, consider two brands facing a recall after receiving back lab results. The first brand carries its lot number through every system downstream of the plant, and the other stops tracking it the moment the product leaves manufacturing. 

The first brand can query its systems and get a lot number, a list of every retailer and distribution center that received it, and shipment dates, within the hour. By the end of day one, pull-back requests are sent to every affected location. By day three, the brand can tell the FDA and its retailers exactly how much product has been recovered and how much is still in transit. 

The second brand starts the same morning with the same lab result and spends that first day just figuring out which system has the shipping records. Lot numbers were captured at the plant but never carried through to the warehouse management system (WMS). Distribution data lives in one format for direct accounts and another for distributor-fulfilled orders. By day three, this brand is still building the list the first brand had within the hour. 

The gap between the two is in whether the operational and data groundwork existed before the clock started running. 

Recall readiness consists of three pillars, all of which are essential. Losing any one of them collapses the value of the other two. 

Nearly every manufacturer assigns lot numbers, because it's required for basic quality control. The harder requirement, and the one that actually determines recall speed, is carrying that lot number through every downstream transaction: the shipment out of the plant, the receipt at a distribution center, the sale into a retailer's system. A lot number that is only used at the manufacturing line is a quality record but doesn’t function as a traceability system.  

Knowing where a lot went means knowing it with confidence. A brand needs to know exactly which retailers, which distribution centers, and in some cases which individual stores received a given lot, down to the unit. An approximation built from total shipment volume gets the scale of a recall roughly right but gets the specifics wrong, and specifics are what a recall requires. 

Knowing where product is doesn't help to transport it back from the retailer. A brand also needs the operational rails already built: carrier relationships that can be activated same-day, retailer processes for accepting returned product, and a way to check that product has actually stopped moving.  

What Happens if a Brand Isn’t Ready for a Recall 

Recall readiness is a chain, and missing any of the three pillars above can cascade into a series of failures.  

Every extra day spent figuring out which lots shipped where is a day the product stays on shelves and in homes. A longer window means more consumer exposure, which means more adverse events, more customer complaints, and in some cases more people affected by the same issue a faster brand would have already contained.  

A wider exposure window also draws more scrutiny from the retailers carrying the product, since they're the ones fielding customer questions the brand hasn't answered yet. 

That retailer scrutiny is the part brands often underestimate. A retailer watching a supplier struggle to figure out which lots are affected is updating its read on that supplier's reliability across the whole relationship. A slow recall response becomes evidence in a much broader conversation about whether this supplier can be trusted to handle other problems when they come up. 

Recall readiness is a data condition, not a single planning document. It's the accumulated result of choices made long before any crisis. Readiness is built the same way traceability is: one accurate transaction at a time, over months of ordinary operations. 