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Why More Supply Chains Are Pairing Forecasting and Demand Sensing 

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Supply chain planning has always depended on looking ahead. Forecasts help businesses prepare inventory, allocate labor, and align operations around expected demand weeks or months before products move. 

Modern supply chains are operating in shorter decision windows than they once did. A retailer promotion can accelerate sell-through within hours. Weather events, transportation disruptions, and fulfillment constraints can reshape inventory needs before organizations can complete their next planning cycle. 

That compression is changing how many supply chain leaders think about planning maturity. Forecasting remains foundational. But more often, organizations are pairing forecasting with demand sensing capabilities designed to identify demand shifts and support faster decisions. 

Large supply chain organizations have been moving in this direction. 

, for example, has publicly discussed using downstream demand signals and short-term consumer movement data to strengthen planning decisions across its network. The broader shift reflects an operational reality many businesses are experiencing firsthand: faster signals only create value when organizations can respond quickly enough for those signals to matter.  

The distinction matters because faster signals alone don't improve performance. Organizations also need the visibility, workflows, and coordination required to act on those signals before disruptions compound. 

So how do forecasting and demand sensing differ and where does each perform best? 

Demand sensing is a supply chain planning approach that uses near-real-time data to detect short-term demand shifts. It supports faster inventory replenishment and fulfillment decisions. 

The signals typically used in demand sensing environments include: 

These data signals help teams detect demand shifts far earlier than what was previously possible with forecasting alone. 

Forecasting Operates on Long-Term Planning Horizons 

Traditional forecasting was designed for 

Forecasting models help organizations answer larger supply chain questions around: 

Most forecasting systems rely heavily on historical sales patterns, seasonality, and broader business trends to estimate future demand across months or quarters. 

 remains foundational to modern supply chain planning because organizations still require long-range operational alignment. 

Demand forecasting uses historical sales data, seasonality, promotions, and trends to predict future demand over weeks or months. Demand sensing uses real-time signals such as POS data, web clicks, search trends, and social media data to detect demand changes in near real time

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Demand Sensing and Supply Chain Planning 

Many organizations assume demand sensing replaces forecasting. In practice, mature supply chain organizations 

 sensing capabilities on top of existing planning methods. Forecasting helps businesses prepare for expected demand. Demand sensing helps businesses respond when conditions begin changing in real time. 

That distinction matters more as planning windows continue shrinking. Retail promotions and social trends can change inventory needs faster than traditional planning cycles were designed to absorb. 

Organizations are increasingly able to detect demand changes quickly, but response capabilities do not always move at the same speed. Demand sensing may surface an inventory issue early, but visibility gaps or disconnected replenishment workflows can reduce the value of the signal before action occurs. 

In traditional planning environments, a retailer promotion that accelerates sell-through on Monday may not trigger a notification until the next planning cycle. In a demand sensing environment, planners may identify that same shift within hours. 

As these capabilities mature, supply chain conversations will continue to expand beyond forecast accuracy alone. Visibility and responsiveness shape planning performance. The advantage has now become reducing the time between signal, decision, and execution. 

One of the most overlooked aspects of demand sensing is that faster signals fundamentally change how planning teams operate day to day. 

Traditional planning environments often revolve around weekly reviews, forecast adjustments, and longer timelines. Demand sensing 

In some organizations, planners move from evaluating operational changes weekly to monitoring conditions continuously throughout the day. This shift can create organizational friction if teams aren’t prepared for it. 

Faster Signals Increase Operational Pressure 

Demand sensing may identify a potential inventory disruption immediately, but planners still need confidence that the signal is accurate enough to justify a departure from normal operations. Transportation teams may need to approve expedited movement. Retail partners may require replenishment coordination. Warehouse labor and allocation decisions may need to shift rapidly. Demand sensing changes the speed and context in which those decisions happen.  

Organizations are being asked to trust faster recommendations while still managing fragmented retailer data, inconsistent visibility, and disconnected workflows. Faster AI signals do not automatically remove those constraints. In some environments, that even creates skepticism, alert fatigue, and pressure to operationalize AI before surrounding processes are ready. 

This is one reason demand sensing relies heavily on operational trust. 

Without that alignment, organizations can generate more alerts, more noise, and more planning fatigue without improving responsiveness. 

Demand sensing tends to create the most value in environments where demand volatility moves faster than traditional planning cycles comfortably can. 

That is especially true in industries like: 

How Retail Volatility Changes Demand Planning 

Retailers increasingly expect faster replenishment responsiveness, stronger inventory positioning, and tighter operational coordination across fulfillment ecosystems. 

 to improve inventory positioning, replenishment timing, and broader fulfillment coordination across their operational networks. 

Organizations operating in fast-moving retail environments often experience demand fluctuations that traditional weekly planning cycles struggle to manage effectively. 

Demand Sensing Isn’t Universally Valuable 

AI discussions can sometimes flatten this distinction. Demand sensing is not automatically valuable simply because it uses AI. In stable-demand industrial environments with long manufacturing lead times, minute-by-minute signal detection may create only marginal operational improvement. 

If production cycles stretch across months and demand patterns remain relatively predictable, the value equation changes significantly. 

That distinction matters far more than the sophistication of the algorithm itself. 

Manufacturing teams often operate under very different planning constraints than retail or CPG networks. Long production lead times, raw material procurement cycles, factory scheduling, and capacity utilization may limit how quickly changes can realistically occur, even when demand shifts are detected earlier. 

In these environments, demand sensing may still improve short-term visibility around inventory movement, but the operational value often depends on whether production planning and supplier coordination can 

 for the signal to materially change outcomes. 

Why Data Flow and Execution Speed Matter More Than Algorithm Sophistication 

One of the most important shifts happening in supply chain planning right now is that forecasting is no longer being treated as a standalone planning tactic. 

The conversation is becoming operational: 

Companies like Procter & Gamble stand out because they forecast effectively and because they have built tighter coordination between sensing, planning, and execution across enormous networks. 

Clean retailer data across POS movement, 

, ASN timing, replenishment patterns, inventory visibility, and transportation activity is what separates meaningful signals from noise.  

When those signals are delayed or inconsistent across trading partners, organizations react to operational noise instead of real demand changes. That delay cascades: it affects inventory positioning, shelf availability, replenishment timing, transportation costs, and ultimately customer experience. 

Better algorithms are the easy part. Building clean, connected visibility across trading partners and the organizational responsiveness to act on it is typically much harder. This is why many AI planning initiatives become challenges long before they become technology challenges. 

5 Questions to Ask Before Investing in Demand Sensing 

Demand sensing can improve response speed, but it isn’t automatically the right fit for every supply chain environment. Before investing in new sensing platforms, supply chain leaders should evaluate whether their data, workflows, and teams can realistically support faster decision-making. 

1. Do We Actually Have Reliable Operational Data? 

Demand sensing systems depend heavily on downstream visibility.  

Retail-Ready Product Requirements for First-Time Suppliers 

When a brand has a strong DTC (direct-to-consumer) business, expanding into major retail often feels like the natural next step. But many new suppliers quickly run into a familiar and frustrating reality. 

A first purchase order comes in, and then problems start surfacing almost immediately: 

Each issue on its own may seem small, but together they lead to delays, deductions, and cascading operational problems that can feel difficult to recover from, often before the product even makes it to the shelf. 

 This is where many emerging brands hit a wall. They realize that just having a good product isn't enough to succeed. Now, it's about removing operational barriers. Having a product that moves through a retailer's operational system with accuracy, consistency, and at scale is a different beast entirely, which is why brands new to retail need to pay attention. 

Retailers like Walmart and Target have raised their requirements significantly. Programs like Walmart's Supplier Quality Excellence Program (SQEP) and stricter validation standards mean suppliers face a more structured compliance environment than they did years ago. 

The challenge? Most suppliers treat this as an operations problem when it's actually a product design problem. Decisions about UPCs, packaging, case packs, and item data shape everything downstream. Get those wrong, and no amount of EDI automation can fix it. 

What Retail Ready Packaging Actually Does 

 Unlike DTC packaging, which arrives with context from ads and reviews, retail packaging has to work alone on a shelf full of products. It needs to do several things simultaneously: 

Your package is the primary communicator. It must convey purpose, differentiation, and value the moment a shopper sees it. 

 for example, improved shelf performance simply by changing its name from "Cubs" to "Kids" and switching to high-visibility colors. 

Retail-ready packaging is designed to minimize labor costs. A well-designed package lets store associates lift the product display directly from the shipper to the shelf without restocking individual units. Big-box retailers prioritize this efficiency. 

Under programs like SQEP, packaging must match specifications for materials, labeling, barcoding, and more. Any deviation, such as a damaged carton, wrong materials, or a mislabeled barcode, counts as a defect and can trigger fines or deductions. 

Eight Critical Areas for Operational Standards 

When you're selling to Walmart or Target at their scale, you can't rely on the flexibility that works in digital commerce. You need a rigorous operational system built around "First Time Quality" (FTQ), which means the retailer receives exactly what they ordered, in perfect condition, exactly when promised. 

Getting to FTQ means meeting strict operational standards across eight critical areas: 

These standards are all interconnected, so new brands should be aware that just fixing one won't be enough to succeed. 

Packaging Often Requires Product Redesign 

 Retail success sometimes means rethinking your product itself: 

All of this shows that packaging should be a top priority when preparing for retail expansion. Many successful DTC brands who don't pay attention to these important layers find their product sales dip exponentially.  

UPCs and GTINs Are the First Major Failure Point 

For many brands entering retail, UPC and GTIN management becomes the first real operational challenge. In DTC, barcode discipline is often loose. Brands reuse identifiers, buy cheap third-party UPCs online, or maintain inconsistent product records across platforms. Those shortcuts usually don't cause problems when shipping directly to consumers, but in retail, that's

Major retailers validate every barcode against GS1 records. If a barcode is duplicated, unregistered, or tied to incorrect product information, the item can be rejected immediately. What looks like a minor data issue can quickly lead to onboarding delays, receiving errors, inventory mismatches, ASN discrepancies, and chargebacks. 

That's because a barcode is much more than a scannable label. In retail, it functions as the product's master identifier across the entire supply chain. Inventory systems, replenishment, warehouse receiving, forecasting, sales reporting, and product data all depend on that information being accurate. Retailers can only process shipments efficiently when the digital record matches the physical product exactly. The barcode, dimensions, weight, pack configuration, and product name all need to align across every system involved. Even a small discrepancy can create manual work, shipment exceptions, or costly downstream errors. 

Item Data Matters for Retail Ready Packaging  

In retail, data is just as important as trucks and warehouses. At big retailers like Walmart, accurate item data keeps everything running smoothly. When data is clean and correct, orders process fast, shipments arrive on time, and products reach shelves without problems. 

But when data is wrong, problems spread quickly. One mistake can cause a chain reaction that slows down receiving, delays inventory, and costs money across the entire network. 

Imagine millions of dollars in inventory sitting at a loading dock, unable to be sold. This happens when what arrives doesn't match what the retailer's system says should arrive. When that happens, receiving teams can't process the shipment. They pull it aside, document the problem, and wait for someone at corporate to fix it. This slows operations, ties up money, and forces people to do manual work that was supposed to be automated. 

Data mistakes come with real financial penalties. Walmart's Supplier Quality Excellence Program (SQEP) treats data errors as defects. A single data error can  trigger fines starting around $200 per order, plus extra fees depending on the type of mistake. 

A small data error in a system can quickly turn into hundreds or thousands of dollars in fines and damage to your relationship with the retailer. 

Additionally, when systems don't show the real state of inventory, stores can have too much product in one location and not enough in another. This means products go out of stock when they shouldn't and customers can't find what they want. Over time, this hurts sales and makes customers trust the brand less. 

Getting orders right the first time requires complete, accurate item data. This includes: 

It’s important to get this data correct, so your products can move efficiently through retailer systems.  

Getting a retail deal and being retail-ready are two different things. A product that performs well on Shopify or Amazon can fail every operational test on day one at Walmart, with wrong UPCs, mismatched case packs, incomplete item data, non-compliant labeling. 

The mistake most first-time suppliers make is treating these issues as operations problems when they're really product decisions. Your choice about case pack size, barcode assignment, packaging design, and item data accuracy happens before your first PO ships. Get them wrong, and no amount of EDI automation or routing guide perfection will fix it. But get those decisions right, and your supply chain has a real chance. 

For more articles helping new brands get started, check out 

From Static to Strategic: How SPS Commerce + Snowflake Turn Retail Data into Action 

If you lead sales, supply chain, or analytics for a retail brand, a lack of data is unlikely to be your issue. The real challenge is translating that data into business and decision-making value.  

Even when you’ve centralized data in a warehouse or BI tool, teams may still be left asking: How do we get ready-to-use sales and inventory data from our retailers? Why do we still have to pull manual reports? Which promotions actually drove sales lift? What data can we use to build our AI strategy? 

That’s the gap the SPS Commerce and Snowflake partnership closes.

By combining retail data and insights from SPS Commerce's intelligent supply chain with Snowflake's AI Data Cloud, you can move your business beyond static reports and siloed data into a single, trusted environment where your data is both accessible and actionable. 

SPS Commerce and Snowflake Partnership Quote 

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The Issue: Data Outside Your Walls Is Slowing Decisions 

Many brands are heavily invested in their data. You may already have: 

In most organizations, that means multiple stakeholders are involved: business leaders who own the results, frontline users who need answers fast, and data teams who build and maintain the Snowflake environment.  

However, data integration is only half the story; the real value comes when that integrated data is structured, normalized, and enriched with business context so it’s easy to turn into decisions. 

On top of that, the data that often matters most for strategic and operational decisions sits outside your four walls: 

This challenge is especially visible for brands operating across large retail ecosystems with multiple channels, partners, and data sources. For companies like 

, understanding what is happening at the point-of-sale across retailers directly impacts promotional strategies, sell-through visibility, as well as the ability to quickly respond to changing demand patterns.  

If your team spends hours logging into portals, exporting spreadsheets, and building one-off integrations just to answer basic questions about what sold, where, and why, then 

you’re leaving time and money on the table.

These manual data practices can quickly turn into:  

That’s where SPS Commerce and Snowflake come together. 

The Fix: See Every Data Point with SPS Commerce + Snowflake 

Together, SPS Commerce and Snowflake can turn your retail data into a living decision engine. SPS delivers clean, POS and supply chain data and insights; Snowflake makes that data available alongside other enterprise sources. 

With SPS Commerce and Snowflake, your teams can move from manual reports and reactive decision-making to answering bigger, more precise questions, like:  

All of these questions, and the answers, depend on having POS and supply chain data from SPS Commerce available in Snowflake.  

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Make Better, Data-Backed Decisions: SPS Commerce + Snowflake Use Cases 

So, what does this actually look like in practice? Here are just a few of the ways brands can use SPS Commerce and Snowflake together to drive better outcomes: 

1. Identify missed revenue opportunities 

With POS and supply chain data and insights in Snowflake, you can quickly see where sales are underperforming by retailer, region, or SKU. This visibility can help you spot gaps between your true performance and benchmarks, and focus your team on the specific customers, products, or locations that require immediate attention. 

2. Improve trade spend efficiency and promotion ROI 

With sell-through data in Snowflake, you can separate real incremental lift from noise. You can compare baseline and promoted performance to understand which promotions and retailers are delivering the strongest ROI. This insight can help you confidently scale high-performing efforts and cut the ones that just shift volume around. 

3. Optimize pricing based on demand trends 

SPS and Snowflake can give you a clearer picture of how consumers actually respond to price. You can see where prices have drifted from your intended architecture, where underpriced items are driving volume without profit, and where strategic price moves could protect or grow margin. 

4. Improve demand forecasts with real sell-through data 

Build your forecasts with a complete data picture. With clear visibility into POS data, you can separate slow movers from fast movers, spot early seasonality shifts, and reduce bias in your planning models. This lets you ground your inventory and production decisions in what shoppers are actually buying, instead of relying solely on shipment data. 

Ready to use retail data insights in a more strategic and higher-value way across your business and supply chain relationships? It’s time to put SPS Commerce and Snowflake to work for your team. 

 to learn how SPS Commerce and Snowflake can drive better, faster decisions across your business. 

SPS & Snowflake  Freddy Guard.png

Join us at Snowflake Summit 2026 at Moscone Center in San Francisco from June 1–4. We’ll also be hosting a 

breakout session on June 1 at 1:00 p.m. PST

, where we’ll explore how connected retail data can drive faster, smarter decisions across the business. 

We’d love to connect in person at our co-sponsored happy hour with 

What Should Fulfillment Teams Ask MAX?

What Should Fulfillment Teams Ask Max?

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Fulfillment teams are operating under a level of complexity that would have been difficult to imagine even a few years ago. 

Retailer requirements continue to evolve. Supplier networks keep expanding. Inventory positions shift constantly across channels. Transportation delays, routing compliance issues, deductions, and service expectations all compete for attention at the same time. 

For many teams, the challenge is navigating the sheer volume of operational data flowing through. 

Teams can spend hours tracking down information before they can even begin solving the actual issue. 

That is where MAX fits naturally into the process. 

MAX Is Built for the Way Supply Chain Teams Actually Work 

MAX is an AI-powered assistant embedded directly into the 

. It helps fulfillment teams move through operational work faster by surfacing insights, identifying patterns, and helping teams evaluate issues with greater clarity. 

MAX has launched with three core capabilities designed to support fulfillment teams across the SPS Commerce network. 

MAX Chat helps teams move through day-to-day operational work faster by surfacing insights, identifying patterns, and guiding issue resolution across retailer and supplier workflows. 

MAX Monitor continuously evaluates transactions, workflow requirements, and exceptions across the SPS network, helping teams stay ahead of disruptions while reducing manual operational effort behind the scenes. 

MAX Connect extends those insights into the broader technology ecosystem, allowing teams to bring SPS network intelligence into the systems they already use every day, including ERP platforms, data environments, and operational tools. 

Because SPS Commerce supports more than 300,000 trading partners across 

, MAX operates with visibility connected to real supply chain activity across the world’s largest retail network. That scale really matters because typically operational issues are not isolated events. They emerge across trading partners, systems, workflows, and timing gaps that are often difficult to identify manually. 

Why Better Prompts Produce Better Operational Outcomes 

One of the most important things fulfillment teams can understand about MAX is that, like most AI systems, the quality of the output improves when the request includes clear operational context. 

A broad question usually produces a broad answer. A detailed operational request creates a much more useful response. 

For example, asking MAX to “summarize this report” leaves significant room for interpretation. Asking MAX to “identify the operational risks most likely to affect OTIF performance over the next month” gives the system a much clearer direction. 

The strongest prompts typically include: 

Most fulfillment teams already think this way naturally. MAX simply helps accelerate and organize that reasoning process. 

What makes MAX especially valuable is not just how quickly it returns information. It is the 

How Fulfillment Teams Are Using MAX in Real Operational Work 

The teams seeing the strongest value from MAX are using it inside the operational moments where complexity drastically slows decision-making. 

What stands out most often isn’t MAX’s fast responses, but rather the depth of operational understanding behind it. 

One fulfillment team asked MAX to review recent fulfillment activity and identify the largest contributors to declining OTIF performance. 

Instead of surfacing isolated shipment failures, MAX identified recurring errors across two separate retail partners that traced back to a shared mapping issue. What initially appeared to be unrelated incidents turned out to be a single systemic operational problem. 

Inside traditional reporting environments, those issues likely would have appeared as disconnected exceptions across multiple systems and partner records. 

This is where teams are beginning to see substantial value from MAX. It helps connect operational patterns that are often difficult to identify manually. 

Preparing Executive Fulfillment Reviews Faster 

Operational reviews often require teams to consolidate fulfillment data from multiple systems while translating that information into clear business priorities. 

One operations team asked MAX for a summary of the past month’s EDI health. 

MAX returned a 30-day operational overview covering more than 3,400 processed documents, organized by volume, document type, and prioritized action categories through a single conversational request. 

What would traditionally require manual aggregation across multiple systems became a significantly faster operational review process. 

Investigating Retailer Deductions More Efficiently 

Retailer deductions are often difficult to investigate because the visible failure is not always the original source of the issue. 

In one example, a team asked MAX to investigate a failed invoice. 

MAX traced the chargeback back to a single reference field that exceeded a retailer’s character limit by four characters. It also identified that the same issue triggered four downstream operational failures connected to the transaction. 

Rather than surfacing a symptom, MAX identified the precise operational correction required. 

Escalations can consume substantial operational time, especially when teams are manually validating transaction activity across multiple systems. 

In one case, a trading partner complaint arrived with 13 separate transaction IDs flagged for investigation due to late shipments. 

MAX reviewed all 13 simultaneously and determined that every transaction had already self-resolved before escalation work began. 

That prevented hours of unnecessary investigation and eliminated a partner escalation that no longer required action. 

Where MAX Fits Into Everyday Operations 

The strongest use cases for MAX are often surprisingly practical. 

The difference often shows up in what MAX returns. 

When a team asks about a failed invoice, MAX does not simply surface an error code. It identifies the specific field responsible for downstream failures. 

When a trading partner complaint arrives, MAX does not just retrieve flagged transactions. It evaluates whether those transactions have already been resolved and determines whether escalation is still necessary. 

Questions IT, Operations, and Finance Should Agree on Before Building EDI In-House 

When a company decides to build EDI in-house, the decision usually starts and ends with IT. IT assesses the technical feasibility, estimates the build cost, and brings a recommendation; operations is consulted briefly; and finance approves or denies the budget. 

However, the information IT has access to covers the technical side of the decision. It doesn't cover the compliance obligations that operations owns, the chargeback exposure that finance manages, or the staffing continuity risks. Those dimensions surface in production, usually at a cost no one budgeted for. 

"The tools are great,” said Eric Shelton, a supply chain strategist at SPS Commerce. “The automation capabilities that are out there are great. But if your teams can't or don't know how to leverage them or use them,  you're not really any better off.” 

This article gives IT, operations, and finance a shared set of questions to work through before a build decision is made, to better understand how to leverage these tools.  

These aren't purely technical questions, and working through them is due diligence in making a build versus buy decision. A build decision made with full cross-functional input is stronger and more defensible than one made on technical assessment alone. 

Which retailers are you trading with today, and which do you realistically expect to add in the next 24 months? 

Each retailer has its own EDI requirements, such as transaction sets, segment qualifiers, timing windows, and testing protocols. A build tuned for your current trading partners will often require significant rework when a new retailer comes online. Operations typically has the clearest view of where retailer growth is likely. IT needs that input before scoping the build. 

Who owns compliance monitoring, and what does the exception-handling process look like when a transaction fails? 

This question sits at the IT/operations boundary. A build that generates errors without a defined escalation path creates downstream problems. IT can build the error notification, but operations needs to own the response process. If that ownership isn'testablished before the build, it gets established reactively, after the first failure has already cost something. 

What is your current chargeback rate, and what does finance consider acceptable exposure? 

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SupplierWiki curates detailed articles to help suppliers win at retail.

Prepping for Walmart Audits

Overview

Articles