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What Is Supplier Diversification? 

 is becoming the norm, not the exception. From shifting trade policies and tariffs to major climate events, organizations are being pushed beyond business-as-usual operations. In a vacuum, treating efficiency as king is fine. But in today’s messy reality, prioritizing only speed and cost minimization can leave companies extremely vulnerable to disruption.  

One disruption risk mitigation strategy that has been gaining momentum over recent years is 

. Supplier diversification involves expanding your supply base in a way that makes your organization less vulnerable when disruptions arise. And according to 

, nearly half (49%) of the 3,000 C-suite executives they surveyed plan to diversify their supply chains in response to macro environment changes. 

Despite the proliferation of modern technologies designed to smooth the supply chain, disruption risk — or at least the perception of it among businesses — continues to rise. In RapidRating’s 2025 Annual Risk Report, 62% of businesses surveyed rated the supply environment as high/very high risk. In the 

Supply chain risks can be divided into two categories: internal and external. Internal risks stem from the company itself, such as inventory mismanagement or a lack of visibility. External risks, by contrast, come from outside the company. According to 

Each of these external risks can wreak havoc on global supply chains. For example, NetSuite notes that the shutdown of the Suez Canal in March 2021 due to a stuck cargo ship halted an estimated $9.6 billion worth of shipments a day for six days. And COVID-19 led to a 

, components that nearly every modern industry relies on, including the automobile industry. 

, which naturally impacted sales, which negatively affected stock prices — illustrating the wide-ranging cascade of devastating effects external risks can have on businesses.  

Supply chains are under pressure to be efficient. But while efficiency strategies are necessary and important, they won’t help businesses survive catastrophic supply chain disruptions. Instead, businesses must also aim to build 

 supply chains (i.e., ones that can continue to function in the face of almost certain disruption).  

“Unlike efficiency strategies, which seek to eliminate excess” NetSuite explains, "resilience strategies embrace redundancy and controls to insure against volatility, by holding inventory buffers, diversifying suppliers, and investing in advanced visibility and monitoring technologies.” 

Efficiency and resilience strategies aren’t an either/or decision. Businesses need strategies aligned to both goals to stay profitable and adaptable.  

Supply chain disruptions can be extremely costly. 

 of more than 1,200 senior executives from 11 industries found that disruptions to engineering, supply, production, and operations had caused companies to miss out on revenue growth opportunities of between 7–11%.  

But improving supply chain resilience isn’t just about what you lose. It’s also about what you gain. The 2023 Accenture survey results revealed that the most resilient companies were able to capture 3.6% greater revenue than the least resilient companies. In addition to this competitive advantage, companies with resilient supply chains are positioned to gain customer trust and loyalty by being so consistent, even in times of crisis. And as a bonus, partnering with a wide range of suppliers can spur innovation that gives you an even greater competitive advantage. 

Supplier diversification is an increasingly important strategy for improving supply chain resilience. Companies that rely on one or two suppliers or even several suppliers within the same geographic area are at a high risk of disruption, explains 

. “A weather-related catastrophe, geopolitical volatility or regulatory uncertainty can disrupt your supply base or at least create bottlenecks,” it continues. “Just-in-time procurement models fall apart under these pressures.” 

Thus, the goal of supplier diversification is to decrease reliance on individual suppliers and to build a larger, more geographically and operationally diverse supplier base. It’s not just a numbers game. The additions must be highly strategic. 

When done well, supplier diversification looks like a procurement team already having approved supplier alternatives in place before disruption arrives. So regardless of whether a business is hit with tariffs or a hurricane, it’s able to adapt quickly to mitigate further disruption. Supplier diversification can be the difference between a disruption being a minor hurdle and a major setback.  

Diversifying Your Supply Chain: Where To Start 

Supplier diversification is a proactive approach to building supply chain resilience. It takes stock of current realities while anticipating what lies ahead. If you’re considering diversifying your supplier base, 

 recommends taking the following steps: 

Keep Learning on The Supply Chain Source 

Speaking of proactive approaches to building a better supply chain, 

 can help you face complexity with confidence by providing: 

Start exploring what The Source has to offer

Key Metrics to Track in Fashion, Apparel, & Footwear 

If you sell to retailers, you already have access to a lot of data: units shipped, units sold, inventory on hand, fill rate, chargebacks, and more. But for most brands, having access to information isn’t an issue, it’s knowing what to do with it. What numbers are useful? What needs to change? 

The most useful fashion supplier metrics fall into four areas: sales performance, inventory, compliance, and deductions. 

The right level of detail matters, too. For apparel, look at performance by style, color, size,  and week when the data is available. A chain-wide average can hide problems that are already affecting sales or inventory. 

Which Metrics Show Whether a Style is Selling? 

 is one of the most useful metrics for an apparel brand. It measures the percentage of received units that have sold. 

Sell-through rate = Units sold ÷ Units received × 100

Review sell-through data weekly during the selling season. Looking at sell-through by style and size can show problems that aren't visible in an overall average. 

For example, a style might have a 65% sell-through rate overall, but that number doesn't tell you whether the remaining inventory is spread evenly across sizes or concentrated in a few slow-moving ones. 

A seasonal sell-through rate of 60% to 80% by the end of the season is sometimes used as a general benchmark, but there is no universal target. The right number depends on the category, price point, retailer, and selling season. 

Taken together, these numbers show whether a product is selling at the expected pace and at the expected price. Then, brands can decide if a fast-selling style needs a reorder, or 

a slow-selling style may need a markdown.  

How Fashion Brands Measure Inventory Performance 

 measure how many times a business sells and replaces its inventory over a year. 

Inventory turns = Cost of goods sold ÷ Average inventory

Apparel businesses often see roughly four to six inventory turns per year, although the appropriate rate varies considerably by business model and product category. 

 is another useful way to look at inventory, as it estimates how long current inventory will last based on recent sales. 

Weeks of supply = On-hand units ÷ Average weekly unit sales

This can be particularly useful during a selling season. If a style has 12 weeks of supply

 but the season has only six weeks remaining, the business may need to adjust its plans. 

GMROI (gross margin return on inventory investment)

 looks at both inventory investment and gross margin. 

GMROI = Gross margin dollars ÷ Average inventory cost

In simple terms, it answers a useful question: 

How much gross margin are you generating for every dollar invested in inventory?

Published industry research commonly puts apparel GMROI somewhere around $1.86 to $3.00, but these figures should be treated as directional rather than as a standard every brand should meet. 

GMROI is most useful when you track it over time and compare similar products. A declining GMROI can indicate that inventory is moving more slowly, margins are falling, or both. 

That gives merchandising and finance teams a reason to look more closely at the assortment before the problem gets larger. 

How Fashion Brands Should Track for Retailer Performance 

Retailers track how well suppliers follow their shipping and order requirements. Some of the most common metrics are: 

These numbers matter because poor performance can lead to chargebacks, lower vendor ratings, and fewer orders from the retailer. 

Tracking these metrics can also help you find problems in your own operations. If your fill rate is low, for example, you can look at which products, warehouses, or retailers are causing the issue and fix the problem. 

How Much Revenue Are Deductions Taking From Your Business? 

Retailer deductions can take a meaningful amount of revenue out of a supplier's sales. Three metrics help show the size of the problem and whether you're doing anything about it. 

 measures deductions as a percentage of sales. 

Deduction rate = Total deductions ÷ Total sales × 100

 measures the percentage of sales or units that are returned. 

 measures how much of the amount you dispute is ultimately recovered. 

A high deduction rate tells you that a significant amount of revenue is being withheld, while a low dispute recovery rate may indicate that you're not recovering money you could potentially claim. A recurring deduction reason can point to an operational problem that needs to be fixed. 

For example, if a retailer repeatedly deducts for routing violations, the answer isn't simply to dispute each deduction. It's worth investigating why the violations keep happening and whether the shipping process needs to change. 

This is where deduction data becomes more useful than a list of individual chargebacks. Looking at patterns can help you separate one-off errors from problems that are costing the business month after month. 

Which Five Metrics Should a Small Fashion Brand Track First? 

Luckily, five metrics can provide a useful view of sales, inventory, compliance, and revenue loss: 

1. Weekly sell-through by style and size 

These metrics can usually be calculated from information a brand already has in retailer portals, shipment records, inventory systems, and accounting records. 

As the business grows, add more detail rather than simply adding more metrics. Start with the total business, then break performance down by retailer, door, style, color, size, or week to find the areas that need attention. 

What is a good sell-through rate for apparel? 

A commonly cited directional benchmark is 60% to 80% by the end of the season, but the right target varies by category, price point, retailer, and season. Review sell-through weekly and, when possible, by style and size. 

GMROI is calculated by dividing gross margin dollars by average inventory cost. Published apparel benchmarks often fall somewhere around $1.86 to $3.00, but these should be treated as directional figures rather than universal targets. 

What's a typical inventory turn rate for fashion brands? 

Apparel businesses often see roughly four to six inventory turns per year. The appropriate rate depends on the business and assortment. Because turns are calculated over a longer period, weekly sell-through and weeks of supply can provide earlier insight during a selling season. 

Why can aggregate metrics be misleading for apparel? 

A style can have a healthy overall sell-through rate while individual sizes or stores are under- or overstocked. Breaking the data down by style, size, door, and week can reveal problems that aren't visible in the overall number. 

The benchmark figures cited in this article are directional industry estimates from published secondary sources, not SPS Commerce network data, and should be used for general orientation rather than as fixed standards.

Most of the metrics above come from data scattered across retailer portals, spreadsheets, and your own accounting system. 

 pulls your sell-through, inventory, and item-level data into one place, by size, by door, by week, so you're not assembling it by hand every time you need to check it. 

Not ready for a full analytics setup? The Supply Chain Source has more resources on 

The Prepared Foods Supply Chain Is a Different Animal Than Center Store 

Shoppers now treat the deli case as a restaurant substitute. In a 2025 FMI survey of 1,571 grocery shoppers, 

. Retail foodservice sales reached $52.1 billion over the 52 weeks that ended Aug. 9, 2025, according to NielsenIQ data that FMI cited. 

Grocers have responded by investing in the category. That means onboarding more suppliers to fill it, such as regional commissaries, local food manufacturers, and co-packers. They are often the least integrated vendors a grocer works with. 

This is where the trouble starts.  Prepared foods runs on a different supply chain than center store (the shelf-stable, ambient grocery aisles), even though it moves through the same purchase order. Its shorter date codes mean tighter delivery windows and less room for error. The failures it produces look like execution problems, but they are usually data problems. 

Why Is the Prepared Foods Supply Chain Different From Center Store? 

Prepared foods differs from center store in three ways: how fast it moves, how often its items change, and what its product data has to carry. A supplier who scales into the category on center-store processes meets all three at once. 

The last row is the one that surprises suppliers. In center store, a late shipment leaves a gap that the next delivery fills. In prepared foods, product that arrives late or gets ordered wrong is often product that gets thrown away. 

How Does Fresh Food Replenishment Change the Forecast? 

Fresh food replenishment leaves almost no buffer between the forecast and the delivery. A shelf-stable supplier plans in weeks of supply. If the forecast misses, inventory absorbs it. In contrast, a prepared foods supplier produces against orders that land days before the product has to be on the shelf, and the product is unsellable within a week. 

Two things follow. First, item data has to be right before the order arrives, because there is no time to fix it after. When the purchase order and the receiving system disagree, the delivery gets rejected or short-received, and the product expires. 

Second, performance measurement gets tighter. Grocers score fresh vendors on the same on-time and in-full measures they use elsewhere, but they tolerate far less. One large grocery retailer working with SPS Commerce 

 after moving to shared vendor visibility. The case study is anonymized and covers the grocer’s whole vendor base, not prepared foods alone. It still shows what the scorecard pressure looks like from the retailer side. 

Why Does Deli Item Data Management Never Stop? 

Deli item data management is a standing load, not a one-time onboarding project, because the assortment never settles. Menus refresh with the season, with local demand, and with whatever the category buyer wants to test next. Each refresh creates work that has to clear the retailer’s validation before the item can be ordered. 

A new or reformulated item generally needs: 

A supplier with a stable center-store assortment does this work once and then maintains it, while a prepared foods supplier repeats it every few weeks, for every banner they sell into. Each banner has its own portal and its own validation rules. Suppliers with no one clearly responsible for item data feel this first, because the work has no owner until something gets rejected. 

What Are the New Grocery Allergen Labeling Requirements? 

Two dates matter for suppliers making grab-and-go items, and both come from regulators rather than retailers. 

Both dates ask the same thing of a supplier. Attribute data has to exist for each item, stay current as formulations change, and travel in a form the buyer can receive. That is a different job than printing a label. 

Why Is Commissary Supplier Onboarding the Pressure Point? 

Commissary supplier onboarding is where the demand growth and the integration gap meet. Grocers expanding prepared foods are adding vendors from the least integrated part of their supplier base. Both sides have a fair position. 

The grocer needs speed. A category that refreshes every six weeks can’t wait months for a vendor to become orderable, and the buyer is measured on filling the case. The supplier often has no electronic data interchange (EDI) capability and no one who owns item data. Asking a 40-person commissary to stand that up on a buyer’s timeline takes real investment. 

Distributors hit the same problem at scale. Gordon Food Service works with more than 3,500 suppliers, and bringing one onto EDI took months, because suppliers varied so widely in size and technical skill. Working with SPS Commerce, the company 

 and improved its readiness for FSMA 204 recordkeeping. Gordon is a foodservice distributor, not a prepared foods maker, so the parallel is directional. The bottleneck it describes is the same one grocers hit in the deli case. 

What Does “Ready” Look Like for a Prepared Foods Supplier? 

Suppliers who scale in this category tend to have the same things in place before the volume arrives. A short self-assessment: 

What counts as prepared foods in a grocery store? 

Prepared foods generally covers items made ready to eat or ready to heat and sold in the deli, bakery, or grab-and-go case. That includes hot bar and salad bar items, sandwiches, rotisserie proteins, packaged meals, and deli salads. Suppliers include in-store production, regional commissaries, local manufacturers, and co-packers. 

How is prepared foods replenishment different from center store? 

Center store plans in weeks of supply against a stable assortment. Prepared foods plans in days, against items that expire within a week. A forecast miss shows up as thrown-away product, not as an empty shelf that the next delivery fills. 

Does the New York allergen law apply to every prepared item? 

It applies to prepackaged food, which the statute defines as food prepared, prepacked, and sold on the same premises. Food packaged after a customer orders it is excluded. How the definition lands on a specific item is a question for a compliance team or counsel. 

What does a grocer look at when deciding whether a prepared foods supplier can scale? 

Buyers tend to look at three things: how fast a supplier can get a new item orderable, how complete and current the item attributes are, and how the supplier performs on on-time and in-full measures once volume starts. 

Want To Talk Through What Your Prepared Foods Growth Requires? 

As prepared foods volume grows, item setup and order accuracy stop being manageable by hand. SPS Commerce works with suppliers, grocers, and distributors on the data layer underneath that growth. That means EDI orders and shipping documents, plus the 

Not ready for that conversation? The Supply Chain Source has 

 for readers working through it on their own. 

This article summarizes publicly available regulatory dates and requirements for general information only. It is not legal or compliance advice. How these requirements apply to a specific product is a question for your own compliance team or counsel.

What Is Network-Trained AI?

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Every supply chain vendor talks about AI now. Far fewer can explain what is actually behind it. Network-trained AI, network intelligence, network effects, agentic AI are all terms that circulate in the same conversations, often the same sentence, because they are related. But they describe different things, and for a buyer trying to evaluate a claim, the difference is the whole point. 

The question that matters most is where the intelligence comes from. An AI system built primarily from one company's history sees the world through that company's experience alone.  

An AI system informed by patterns across a large trading network sees something wider: What tends to precede a rejection, which exceptions escalate, where recurring problems originate. That difference shapes what the system recognizes, what it recommends, and how much experience it can draw on when something unusual happens. 

Here is what network-trained AI means, where the terminology gets tangled, and what to ask before taking a vendor's claim at face value. 

 lays out several ways AI and machine-learning systems can operate in multitenant environments. A model can be built for one tenant, shared across many, or built on a common foundation and customized per customer. That framing is a useful starting point for supply chain. 

Network-trained AI learns from patterns that extend past a single company's experience. Instead of one supplier's orders, shipments, invoices, exceptions, and compliance issues as its only frame of reference, the intelligence behind it draws on activity across an entire trading network. 

Siloed AI has a narrower field of view. It works from one company's data and history. That can be genuinely valuable, especially where the use case depends on proprietary information the company alone holds. But the system can only learn from what happened inside that one environment. 

Think about experience in any profession. Someone who has handled a problem five times has real experience. Someone who has seen variations of that same problem thousands of times has a far larger body of evidence to draw from, and supply chain problems repeat constantly across companies, retailers, categories, and trading relationships. That is where breadth starts to matter. 

Supply chains generate enormous amounts of data. Volume alone does very little. What does move the needle is whether that data contains enough relevant examples to surface a meaningful pattern. 

Take a supplier facing an unfamiliar compliance issue. Inside its own systems, it may have only a handful of comparable incidents to work from. Inside a larger network, similar issues may have already played out many times across other suppliers and other trading relationships. That history becomes context: The conditions that tend to precede a rejection, the exceptions that usually escalate, the point where recurring problems tend to start. More relevant experience with the exact kind of situation the AI is being asked to understand. That distinction gets even sharper when conditions shift.  

Models built heavily on historical, siloed information can struggle once the environment moves faster than their history does. 

These terms get bundled together constantly. Each one answers something different. 

The fastest way to keep them straight is to ask what question each one is really answering. 

These capabilities overlap. They are not interchangeable. A system can benefit from network intelligence without being agentic. An agent can run entirely on one company's data. A network can create real value without touching AI at all. 

, which examines competition and market structure across the AI supply chain, points to network effects, economies of scale, and high fixed costs as forces shaping the AI market. That is an economic observation about how markets develop. It is not a technical claim about how an AI system is trained. Keeping the two apart makes vendor conversations far easier to evaluate. 

Five Questions to Ask About Network-Trained AI 

We don’t need a data science background to get past the terminology. A handful of direct questions will tell you most of what you need to know. 

1. How broad is the network behind the AI? 

Ask how many organizations, trading relationships, transactions, or years of history feed the intelligence behind the system. The goal is not the biggest number. It is understanding the depth and diversity of experience actually represented. 

2. How does information from the broader network improve the result? 

This is the question that matters most. A vendor should be able to name exactly what the broader network lets its technology recognize, predict, or recommend more effectively. If the only answer is "a lot of data," keep asking. 

3. How relevant is that network to my business? 

Scale without relevance only goes so far. A network with deep experience across your retailers, transaction types, product categories, and operational challenges can beat a much larger dataset with little connection to the problems you actually need solved. 

4. Does the system learn from broader patterns while keeping customer data protected? 

Network intelligence and data privacy have to coexist. Ask how customer information is isolated, anonymized, aggregated, and governed, and which parts of the system rely on shared data versus customer-specific data. A vendor should be able to walk you through that architecture in plain terms. 

5. What exactly does network-trained mean in your product? 

Do not assume every vendor uses the term the same way. Ask whether network data feeds model training, fine-tuning, retrieval, benchmarking, inference, or recommendations. The distinction sounds technical, but it tells you far more about the architecture than the label ever will. 

What Network Intelligence Looks Like in Practice 

 draws on the context of an intelligent supply chain network. That includes hundreds of thousands of trading connections, billions of transactions, and decades of supply chain expertise, because most supply chain problems are not unique to one company. 

Retailers and suppliers hit the same recurring patterns in orders, shipments, item data, compliance requirements, deductions, and trading partner communication, again and again. A network provides the breadth of experience needed to actually understand those patterns instead of just logging them. 

What happens next is the part that matters. Network data becomes valuable the moment it turns one company's transaction into something understandable and actionable: An unusual pattern gets recognized, an exception gets context, a likely problem gets identified, and the next step becomes clear. That is the whole promise of network intelligence, and it only works because the network has seen the pattern before. 

No single architecture wins every problem. Some data should stay tightly isolated. Pricing, sourcing strategy, proprietary product information, and other competitively sensitive information often need firm boundaries, and regulatory requirements can add further constraints on top of that. 

Other situations favor broad network context without much debate. Compliance patterns, transaction errors, and fulfillment exceptions get easier to recognize when a system has experience across many trading relationships, not just one. 

So, the better question is whether the AI has access to the right experience for the decision you are asking it to make instead of whether or not the network-trained AI is superior to others.  

Frequently Asked Questions Regarding Network AI 

Is network-trained AI the same as a network effect? 

No. Network-trained AI describes how broader network data or patterns feed an AI system's intelligence. A network effect is an economic concept: A network becomes more valuable as participation grows. The two reinforce each other. They are not the same thing. 

Does agentic AI require network-trained data? 

No. Agentic AI describes what a system can do, particularly its ability to pursue goals and take action across multiple steps. One agent could run entirely on a single company's data. Another could draw on intelligence built across a far broader network. Both are agentic. 

How can I evaluate a vendor's network-trained AI claim? 

Start with specifics. Ask about the size and relevance of the network, how network information actually improves the output, how customer data is protected, and exactly where network data enters the system. The quality of the explanation matters as much as the numbers. A vendor should be able to describe the advantage in terms of real business problems, not just repeating that it has more data. 

Not necessarily. Scale creates more potential examples, but relevance, quality, and context matter just as much as volume. A network with deep coverage across the retailers, suppliers, transactions, and operational scenarios relevant to your business can deliver more useful intelligence than a larger but less relevant dataset ever will. 

The terminology around AI will keep changing. For supply chain leaders, the important questions will remain practical:  

Those questions make it much easier to separate a meaningful advantage from a good piece of marketing. And they are worth continuing to ask. AI, automation, and network intelligence are changing quickly, along with the expectations placed on the teams using them. 

, SPS Commerce's library of articles, guides, and how-to's, covers this ground in more depth: how network intelligence works in practice, what agentic AI actually changes about a team's day to day, and how to tell a real network effect from a good sales pitch. It's a place to keep coming back to as the terminology shifts and the vendor claims pile up. 

If you're evaluating a specific tool right now, start with the five questions above and bring them into your next vendor conversation. And if you want to see what network-trained intelligence looks like when it's built into daily supply chain work, 

What's New on the Platform — SPS Analytics 

____________________________________________________________________________________ 

If you ask a brand what major issues they’ve been having, they rarely say that they don’t have enough retail data.  

Instead, the problem is usually what that data looks like when it arrives. Retailers report on different schedules, using different item numbers and location structures, with fields that seem identical on the surface but measure slightly different things. It’s the brand’s job to locate the files, reconcile the definitions, rebuild a spreadsheet, and decide which version is correct. 

Having retailer data is not the same as having decision-ready insight. Closing that distance is what the modernized 

The new SPS Analytics experience is not a visual refresh. The modernized experience reduces the number of steps between opening SPS Analytics and understanding what is happening across the business. Once the latest retailer data has loaded, dashboards open in seconds once retailer data has loaded. 

From there, the work stays in one place. Filters are applied while you are looking at the data, not configured in advance and rerun. Moving from a high-level result to the retailer, product, region, or location behind it does not require rebuilding a report, and moving between related views does not mean starting over. 

That difference matters most in the work that repeats, such as performance reviews, routine preparatory work, or the second look at an account that seemed soft last week. When each of those takes fewer steps, teams run them more often and see issues sooner. 

Before any numbers reach a dashboard, SPS cleans, normalizes, and aligns the sales, inventory, product, and location information coming from each retailer. Standardized mapping connects supplier items and locations to retailer items and locations across trading partners, so a comparison between two accounts is a real comparison rather than an approximation. 

From there, the view bends to the business. Configurable attributes and hierarchies let each brand organize products and locations the way its teams already think about them, whether that is by category, channel, collection, region, or a structure specific to how the company operates. 

: SPS refreshes daily for retailers that provide daily data and weekly for many others. But store-level visibility is only available when the retailer supplies store-level information, and margin visibility depends on the necessary retailer data or supplier-provided cost information. 

 A configurable home page puts the KPIs, charts, and favorite dashboards a team checks most often in one place. The recurring weekly review starts assembled instead of rebuilt. 

 Filters are applied in view, and a drilldown moves from retailer to product to region to store without a rerun. Because those filters carry across the session, the train of thought survives the move from one view to the next, which is usually where the useful finding lives. 

 On-hand inventory, sell-through, and weeks of supply sit alongside current sales performance. That context is what turns "sales are down" into "sales are down at these accounts, and here is the inventory position behind it."  

 Teams can review top and bottom performers, mix shifts, sell-through differences by account or channel, and year over year comparisons where the underlying data supports them. SPS Analytics surfaces where performance is moving and gives teams a way to investigate what may be contributing to it.  

 is a small team managing a catalog of roughly 120 SKUs across a growing set of retail relationships. Looking at assortment side by side in SPS Analytics, the team could see how differently two major accounts were carrying the brand. One was stocking 14 items, while the other carried the full catalog across 104 locations. 

Once made visible, that gap is not hard to understand. But the visibility is difficult when each account reports separately and no one has time to line the numbers up by hand. With assortment, sell-through, and weeks of stock in a consistent view, the team can prepare for buyer conversations with account specific evidence and identify where a reorder conversation may be worth exploring without building a report first. 

"It's been a phenomenal tool to make decisions quicker and more efficiently and help grow our relationships with retail customers."

A new experience is only worth as much as the decisions it improves. We've compiled a list of questions the new SPS Analytics is helping answer 

Where is performance falling short, and what deserves attention first?  

Revenue and unit performance by retailer, product, region, and SKU makes it possible to investigate underperformance while there is still time to respond, redirect sales attention toward the accounts that matter most, and review product mix with evidence rather than instinct. 

Where might inventory and demand be out of alignment?  

Sales, on-hand inventory, sell-through, and weeks of supply together help teams prioritize which situations to investigate, which retailer conversations to have, and where execution at the shelf deserves a closer look. 

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