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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 

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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. 

When inventory is constrained, where should the next units go?  

Retailer, region, channel, product, and store-level performance gives allocation discussions a shared starting point and lets teams compare current demand against existing supply priorities. 

More dashboards is not the goal. A large reporting library that nobody owns tends to go unread, while a small set of trusted views that a team returns to every week can change how a business runs. 

What turns that view into results is the rhythm a team builds around it. It usually looks something like this: 

What these steps require is a starting point that everyone trusts and can reach without a week of preparation. 

The SPS Analytics experience shortens the distance between opening a dashboard and knowing what to do next, which makes the rhythm above sustainable rather than aspirational. 

If you are considering where to begin, resist the urge to start everywhere at once. Pick one retailer relationship, one question genuinely worth answering, and one person accountable for the follow up. Get that loop working, then widen it. 

 experience? Request a demonstration and we'll walk through it with your business in mind. 

Five Decisions Brands Can Make With SPS Analytics

Oftentimes, brands selling into retail already have a rhythm for reporting and analytics, but for many of them that is where the routine begins and ends. There is a practical reason for that. Reports describe what happened, but deciding what to do about those insights is separate work that can get overlooked without clear ownership. 

That is where a lot of the value in retail data gets lost. A large reporting library that nobody owns tends to go unread, while a smaller set of trusted dashboards that a team acts on every week can change how a business runs. What matters is not how much reporting a team produces, but whether the review ends in an action. 

SPS Analytics turns fragmented retail sales, inventory, and item performance data into a clear, connected foundation. Information from retailers, distributors, ecommerce marketplaces, and owned channels is standardized, enriched, and aligned to your product catalog, so analysis stays consistent from one trading partner to the next. Here are five questions that foundation makes easier to answer, and the decision that follows each one. 

Inventory problems are usually easy to explain after the fact: Stock builds up in slower locations while demand grows somewhere else. But by the time the gap is revealed, it has already cost something, either in markdowns on one side or lost sales on the other. 

In SPS Analytics, on-hand inventory, sell-through, and weeks of supply data sit next to current sales, so a slowdown and the stock position behind it show up in one view. Where the retailer shares store-level data, teams can run the same comparison by store and by SKU. When stock is tight and units have to be split across accounts, you can filter data by retailer, region, channel, and product to give that discussion a shared starting point. 

The choice in front of the team is which inventory situations get attention this week, and what response each one gets. The options are usually a short list: Raise it with the retailer, change what ships next, move units toward the locations where demand is stronger, or check whether the problem is shelf execution rather than supply. When stock is tight, the same evidence helps inform which accounts should receive the next units. 

SPS Analytics shows where inventory and demand look out of step so brands can act accordingly. 

Which Products or Stores Are Underperforming? 

A retailer can look healthy in total while specific SKUs, regions, or doors quietly drag. Aggregate numbers average that away, so the weak parts stay hidden inside a figure that looks acceptable. 

Revenue and unit performance break out by retailer, product, region, and SKU. Teams can rank top and bottom performers, compare sell-through across accounts or channels, and see where the product mix has shifted. When possible, brands can see year-over-year comparisons. Margin appears where the retailer supplies what is needed or the supplier provides cost information. Store-level detail is available where the retailer shares it. 

The decision is where the sales team should direct its attention, and which underperformance is worth acting on at all. Some of it is seasonal or expected, and leaving it alone could be a valid answer. The rest usually traces to the following causes: The product is not selling, it is not stocked, the mix has moved toward lower-margin items, or one region is behind the others. 

Promotions tend to get planned carefully and reviewed loosely, and the results often surface weeks after the event has closed. Teams can end up repeating what felt successful and dropping what felt flat, without a clear read on either. 

Sales and unit performance across the promotion window can be compared against the weeks before and after it, which shows how that period differed from a normal one. The same comparison holds at the retailer, product, and region level, so a team can see where the response was strongest instead of treating the event as one number. Inventory position going into and coming out of the window sits alongside it. Sell-through during the promotion can be set against a typical week. 

The choice is which events are worth running again, and in what form. That usually breaks into narrower questions: whether the accounts that responded are the ones the event was aimed at, whether the products that moved were the intended ones, and whether there was enough inventory in the right places to meet the demand. A promotion that sold well but ran out early is a different problem from one that never moved at all. 

SPS Analytics shows what happened across the promotion window. Separating a real response from normal seasonal movement requires interpretation, and that work stays with the team. 

Where Might Assortment Need To Change? 

Two retailers rarely carry a brand the same way. One could take a narrow selection, while another could take most of the catalog, and a third could stock the range but only in some of its locations. Those differences are hard to see when each account reports separately, so a catalog that looks well distributed in total can be thin in specific places. 

Performance breaks down by retailer, product, region, SKU, and location, which shows what is carrying the business rather than what the team assumes is carrying it. Assortment depth can be compared across accounts, so the gap between a partial selection and a full one is visible in one place. Sell-through and weeks of stock by item separate two situations that look the same in a sales report: an item that is not selling, and an item that is not stocked. 

, a small team managing a catalog of roughly 120 SKUs, found one major account carrying 14 items while another carried the full catalog across 104 locations. That gap had existed for a while, but it became actionable once the two accounts could be compared in the same view using SPS Analytics. 

"It feels like you actually know what you're doing versus guessing."

Using this information, brands can decide which assortment gaps are worth a closer look and which are fine as they are. Some accounts carry a narrow selection for good reasons, including shelf space, category strategy, or a decision the buyer still stands behind. For the rest, the team decides which items to build a case for expanding and which are candidates to scale back.  

What Should the Account Team Prioritize Before a Retailer Meeting? 

Line reviews and buyer meetings often get prepared from whatever was available that week, such as partial information, or a report that is already a couple of weeks old. The team ends up responding to the buyer's numbers instead of arriving with its own, and the meeting turns into a review of the retailer's view of the relationship. 

Sales, inventory, sell-through, and weeks of stock can be filtered to the single account the meeting is about, so the prep matches the conversation. The same items can be compared against the rest of the business. That shows whether a slow seller at one retailer is slow everywhere or only there. Tables can be exported for use in meeting preparation and follow-up. 

Teams can decide what is brought to the meeting and what to leave out. A line review rarely has room for everything, so the work is narrowing to the few points worth the buyer's time. An ask backed by the retailer's own sales and inventory numbers is easier for a buyer to act on. 

For many brands, these decisions don’t require new data. Instead, they require the data they already have to be organized so that acting on it takes less work than assembling it. 

The teams that get there tend to review the signal, add the context only they have, choose an action, name the person who owns it, and come back later to see whether it worked. SPS Analytics supports the investigation and the evidence behind it. The decision stays with the people who know the business. 

Ready to see how SPS Analytics can help you transform your retail data into actionable insights? Learn more about 

What SPS Analytics Means for Your Teams

Retail sales and inventory data usually gets filed under analytics. It arrives as a feed owned by the data team, and everyone else submits requests to view it.  

That setup, however, has unintended costs. The analytics team becomes a queue, working through questions other people could answer themselves. Meanwhile, the sales manager, the inventory planner, and the account lead wait for numbers that could arrive after a long delay.  

The brands that get the most from retail data are not necessarily the ones with the best tools, but rather are the ones where teams can each ask their own questions of the same numbers, with no request ticket  holding things up.  

However, this isn’t as easy as it might seem. Retailers report on different schedules, with different item numbers and different location structures. A comparison between two accounts means something only after that data has been aligned — which is where SPS Analytics comes in. SPS Analytics helps clean, normalize, and align retailer sales, inventory, product, and location information, so one view can serve teams asking very different things of the data. 

In this article, we analyze what four different teams in one brand might ask of the same data, and what changes when each of them can ask it directly. 

Sales teams usually know what shipped, but much less about what sold through. The questions they ask of the data are account specific.  

The useful measures for these teams are the ones tied to a single account.  

Using SPS Analytics, the team arrives at buyer meetings with its own evidence instead of simply responding to the retailer's numbers. You can filter sales, inventory, sell-through, and weeks of stock to the single relevant account. Additionally, you can compare these against how they perform across the rest of the business, and assortment depth can be compared across accounts. Data can be exported for use in meeting preparation and presentations. 

, a lean team managing roughly 120 SKUs, found one major account carrying 14 items while another carried the full catalog across 104 locations. That difference became something the team could act on once both accounts were seen in the same view. 

Business Intelligence (BI) and Analytics Teams 

The first questions for BI and analytics teams tend to be about capacity, since so much of their time is taken up by preparing the data for analysis.  

BI and analytics teams look for item and location keys that reconcile across retailers, as well as coverage and history deep enough for a real comparison and a refresh schedule steady enough that a missing file gets flagged rather than averaged in. Most of all, they want standardized data they can work with consistently across the reporting and analysis tools their company already uses. 

SPS Analytics helps to standardize the mapping before the data reaches a dashboard, so supplier and retailer items and locations line up across accounts. Custom attributes and hierarchies let the structure follow the business rather than each retailer's filing system.  The standardized foundation makes it easier to add retailers, metrics and future capabilities without requiring customers to rebuild their reporting experience. 

Much of the cleanup happens upstream as well. Roughly 10-15% of weekly retailer data needs some fix before it is usable, and SPS monitors data receipt, checks for duplicates and gaps, and translates retailer language into one set of terms.  

Planning teams work from a forecast, and a forecast is only as good as the demand signal behind it. Their questions tend to be about the gap between the plan and what is actually happening at retail. 

These teams often consider sell-through to be a primary signal, because it reflects what shoppers bought rather than what shipped into a retailer's network. Weeks of supply sets sell-through against the inventory already in place, separating a real demand shift from a stocking decision.  

They also analyze store and SKU patterns where the retailer supplies store-level data, since a regional problem and a national one call for different responses. When units have to be split across accounts, performance by retailer, region, channel, and product is what makes the comparison possible. 

With SPS Analytics, demand planning and supply chain teams can check what sold rather than what shipped, as weeks of supply sit next to on-hand inventory and current sales. They can also spot imbalances between stock and demand while there is still time to move units or raise the issue with the retailer. That signal is an input to the forecast, not a replacement for it. SPS Analytics gives the planning team a current read on what is selling, but building the plan remains their work. 

What leaders need to know is whether the process underneath the numbers is working, so their questions tend to be about timing and alignment rather than any single metric. 

Leaders tend to look at revenue and unit performance by retailer and by channel as the starting point, which shows which relationships are growing and which have gone flat. Sell-through across the portfolio answers whether the shoppers are buying the product, and category and brand-level views matter more than SKU detail at this level.  

Leaders also pay attention to account concentration, since a business where two retailers carry most of the volume runs a different risk than one spread across twenty. Where the retailer data supports it, executives often want a year-over-year comparison and portfolio margin. 

SPS Analytics provides the measures a leader checks most often, with fills that can be filtered by retailer, product, region, and other available dimensions. 

The larger change is in the meeting of the teams. When sales, planning, and analytics are working from the same aligned data, a review can start from a shared picture rather than settling whose number is right first. 

What Changes When Everyone Is Using the Same Data Foundation 

Four different teams will have different priorities and ask four different sets of questions. The difference is whether those questions run against one aligned dataset or four separate versions of the truth. With SPS Analytics, the questions stay different, but the answers reconcile. 

The most visible difference is that meetings can start from a shared view instead of first working out whose number is right. This is made possible by standardized mapping and shared definitions, because a comparison means the same thing to everyone in the room. 

 and weekly for many others. Store-level visibility is available only when the retailer supplies store-level information, and margin visibility depends on retailer data or supplier-provided cost information. 

The four sections above describe the same underlying data serving an account manager, a BI lead, a planner, and an executive, each asking questions that the others may not think to ask. That is business visibility, not a reporting product for the data team. 

SPS Analytics supports the investigation and supplies the evidence. The decisions stay with the teams who know the accounts, the plan, and the business. 

Ready to see where your team fits? Explore the 

 use cases most relevant to your role. 

Demand Forecasting vs. Demand Planning: Key Differences 

 are two supply chain terms that sound plausibly interchangeable. However, while they are closely related, they refer to distinct parts of the 

 process, which aligns an organization’s sales expectations, operational capacity, material availability, and financial goals around a single game plan.  

From manufacturers to retailers, a smooth supply chain relies on accurate demand forecasting and planning. When done well, these processes support better decision making that saves time and money. 

 defines demand forecasting as “the process of using data, analytics, insights, and experience to estimate future product and service needs.” Or as 

 describes it, demand forecasting is “fundamentally about predicting what people are going to want, how much, and when.” Similar to a weather forecast, a demand forecast is an educated guess about what will happen in the future. 

 notes that no demand forecasting model is perfect, it offers the following best practices: 

Demand forecasting is part of demand planning. According to the 

, demand planning “takes the forecast further by integrating it into the business strategy, aligning stakeholders around a shared set of expectations, and determining the actions needed to respond to that demand.” This creates a “realistic and actionable view of future demand so the organization can align supply, resources, and investments accordingly.”  

If demand forecasting asks what demand will be, demand planning asks what needs to be done to meet that demand. 

 demonstrates how demand forecasting fits into demand planning: 

Like demand forecasting, demand planning isn’t static. It must be able to react to environmental shifts, like world events and consumer trends.  

What is the difference between demand forecasting and demand planning? 

Demand forecasting predicts what consumers are going to want, how much they’re going to want, and when they’re going to want it. Demand planning uses demand forecasting to create a plan for how to meet that demand.  

What are the benefits of demand forecasting and demand planning? 

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