In this article, learn about:
The transition from DSS to Scintilla’s omnichannel architecture
Deep-dive technical configurations for store demand forecasts
Advanced procurement planning with the Order Forecast dataset
Utilizing Custom Datasets for targeted inventory segmentation
Functional differences between Scintilla Basic and Charter for replenishment
Key features of Walmart Scintilla for Walmart business
One of the great sins of retail is not having the product available when a customer wants to purchase it. While simple in concept, maintaining sufficient store inventory can be a complex and sometimes daunting task. This article provides a comprehensive guide to the components of fulfillment, supplier expectations, and the powerful analytical tools within Scintilla (the successor to DSS) necessary to ensure product availability for your Walmart customers.
Walmart Scintilla is Walmart's proprietary comprehensive data analytics product suite within Walmart Data Ventures, designed to provide full visibility into in-store and online performance data for consumer packaged goods (CPG) companies. Scintilla Basic offers access to standard operational metrics and pre-packaged reports for Walmart business insights, while Scintilla Charter includes advanced features such as an API key for data integration and access to unique datasets. Walmart Scintilla enhances supplier-retailer collaboration and improves customer-centric decision-making.
The Evolution of Walmart Reporting: From DSS to Scintilla
For decades, the Decision Support System (DSS) was the primary vehicle for suppliers to measure sales, supply chain performance, and replenishment. However, the architecture of DSS dated back to the 1990s, making it increasingly laggy and inaccurate regarding eCommerce numbers. Starting in early 2024, Walmart began sunsetting DSS in favor of Scintilla, its new inventory management software.
The push toward Scintilla was driven by a need for better tracking across omnichannel environments. Scintilla is split into two primary tiers: Luminate Basic (unpaid) and Luminate Charter (paid). While Basic provides essential replenishment datasets, Charter offers “Net New” data assets such as hourly store inventory and detailed shopper behavior. For replenishment purposes, understanding how to navigate the Report Builder app within Scintilla is the single most important skill for a modern supplier. Suppliers can use the data dictionary within Scintilla to understand DSS field names, build accurate reports, and integrate data effectively for better retail analytics.
The Lifecycle of a Purchase Order
Understanding the life of a purchase order (PO) is crucial because of the many moving parts involved in supply chain fulfillment. Long before a supplier receives an EDI transmission, Walmart’s Replenishment Management Teams have done extensive research. To make effective restocking decisions and maintain overall inventory management, it is essential for suppliers to keep accurate records of their current inventory. In the new Scintilla ecosystem, suppliers have two primary datasets to help them “see into the future” of their supply chain:
Store Demand Forecast: Projections of what Walmart expects to sell to customers through stores over the upcoming weeks.
Order Forecast: Projections of what the retailer expects to order from the supplier.
The Store Demand Forecast and Demand Forecasting
The Store Demand Forecast is Walmart’s first pass at predicting consumer behavior. It utilizes two years of historical sales data, accounting for non-recurring events, modular adjustments, and store count changes. This report is the basis for all replenishment planning. Calculating average daily usage from sales data is essential for determining when to reorder stock. The reorder point formula incorporates average daily usage, lead time, and safety stock to help suppliers decide when to place new orders and prevent stockouts.
The Order Forecast (Formerly Supply Plan)
While the Demand Forecast focuses on the consumer, the Order Forecast offers insight into what Walmart believes it will order from the supplier. This document builds off the demand projections and factors in store inventory position, markdown strategies, and changes to safety stock. Lead time—the time it takes for new stock to arrive from suppliers—is a key consideration in order planning. Using the economic order quantity (EOQ) method helps suppliers determine the ideal order quantity to minimize ordering and holding costs.
It is important to note that the Order Forecast is a planning tool and not a legal promise to buy. Suppliers should audit these projections against actual orders to determine accuracy. Significant variances should be addressed with your Replenishment Manager using Scintilla data as supporting evidence.
Fulfillment and the OTIF Component
Execution is just as essential as planning. Walmart’s On-Time In-Full (OTIF) program ensures that forecasting results in the correct amount of product arriving when expected. Effective inventory control is crucial for tracking stock levels and preventing both stockouts and overstock situations, supporting efficient supply chain management.
Conducting regular inventory audits helps identify and prevent inventory issues, such as stock discrepancies and data inaccuracies, ensuring accurate stock levels and smooth operations. While Basic users access OTIF through the Retail Link OTIF Scorecard, Charter subscribers can utilize the OMNI OTIF dataset within Scintilla. This allows for a deeper investigation of order fulfillment to both Distribution Centers (DCs) and Fulfillment Centers (FCs) over the last 52 weeks.
Related Reading: The Ultimate Guide to OTIF
Technical Guide: Building the Store Demand Forecast
The Store Demand Forecast is critical for determining how much inventory stores must keep on hand. Timely insights from these forecasts enable suppliers to make proactive decisions and optimize stock management. Unlike the static templates of the old DSS, Scintilla requires a more manual configuration using specific “Attributes” or business elements.
Step-by-Step Report Configuration:
Dataset Selection: Navigate to Report Builder > Dataset > New Report and select Store Demand Forecast.
Business Elements (Columns): You must select specific attributes to populate your report. Two mandatory elements are:
Walmart Year Week Number: The specific WM week number of the forecast, providing visibility up to 104 weeks forward.
Final Forecast Each Quantity: This is the most accurate metric, calculated as Sales Forecast Each Quantity + Total Adjustment Each Quantity.
Required Filters: You cannot run this report without a filter. You must enter your 6-digit vendor number and select a Forecast Walmart Week as a required dimension.
Submission: After naming your report (e.g., "13-Week Demand Forecast"), click Create. Reports typically take 20-70 minutes to process.
Technical Guide: Building the Order Forecast
The Order Forecast dataset provides the granular detail needed for factory planning and procurement. With this detailed data, timely and accurate replenishment orders can be triggered, helping to prevent stockouts and optimize warehouse operations.
Utilizing DSS Aliases
A common hurdle for suppliers is that Scintilla renamed many traditional DSS terms. However, the old names—known as aliases—remain searchable in the Scintilla filter panel.
Key Attributes for the Order Forecast:
DC/Store Number: (DSS Alias: Whse Nbr or Store Nbr). This allows you to pull reports for specific warehouses or individual stores.
Order Place Date: (DSS Alias: Plan Order Date). This is the projected date the order will be generated.
Scheduled Arrival Date: (DSS Alias: Plan Receive Date). This is the recommended date for delivery at the Walmart facility.
Order Each Quantity: (DSS Alias: Suggested Order Qty). This shows the units ordered, derived from vendor pack configurations.
Charter-Only Enhancements
For suppliers with a Charter subscription, five additional predictive attributes are available that were not present in DSS:
Available Ship Date: The date the item is available at the DC or Vendor.
Forecast Sequence Number: A unique ID generated by the forecasting system for a specific store/item combination.
Need Arrival Date: The specific date the inventory is required at the store level to prevent an out-of-stock.
Need Ship Date: The deadline for shipping to ensure on-time arrival.
Scheduled Ship Date: Used for fetching precise forecasting values.
Access to real-time data through Charter enables suppliers to respond quickly to market changes and optimize inventory replenishment decisions.
Advanced Inventory Management: Custom Datasets
One of the most powerful features added to Scintilla Basic is Custom Datasets. Advanced tools like automated software and integrated inventory management systems can further enhance the effectiveness of custom datasets by automating data analysis and optimizing replenishment processes. This allows suppliers to combine tables from multiple pre-built datasets for a holistic view of their business.
Case Study: Data Segmentation for Low Instocks
If a supplier notices low instock levels but high weeks of supply, they can build a Custom Dataset to identify microtrends. By combining the Store Sales and Store Inventory tables, a supplier can pull a report with the following attributes:
Prime Item Number
Store Number
POS Quantity – This Year (DSS Alias: POS Qty)
Store On Hand Quantity – This Year (DSS Alias: Curr On Hand Qty)
This segmented data allows you to identify specific geographic regions experiencing stock imbalances or sales anomalies, enabling targeted interventions before a full out-of-stock occurs. This approach also helps suppliers proactively identify and address inventory issues, such as stock discrepancies and data inaccuracies, before they escalate into major stockouts or overstock situations.
Managing Instocks in Scintilla
Tracking instock levels is the cornerstone of replenishment. Scintilla offers metrics that almost exactly mirror the old DSS definitions:
Instock %: Calculates the percentage of valid stores that have at least one unit on hand.
Replenishment Instock %: The most common metric, comparing inventory to forecasted daily demand to see if current stock covers future sales.
Store Replenishment Instock %: Found within the Vendor Scorecard, this measures a store’s capacity to meet demand; stores are “Out of Stock” if On-Hand Qty is lower than the Demand Forecast.
Effective stock replenishment is essential for reducing stockouts, preventing overstocking, and managing safety stock. Replenishing stock at the right time helps meet customer demand and avoid lost sales. Stockouts frustrate customers and can lead to lost sales as unhappy customers may turn to competitors, while overstocking increases carrying costs and the risk of unsold inventory expiring or becoming obsolete. Safety stock is the inventory kept on hand to cover unexpected spikes in demand or supply chain delays. The reorder point (ROP) method helps determine when to replenish stock to avoid stockouts.
For those needing deeper historical context, Charter users can access Replenishment Instock Numerators and Denominators for both “This Year” and “Last Year”.
Best Practices for the Scintilla Workflow
Download Weekly: Scintilla’s “fuzzy dates” (like “Last Week”) are relative to the time of the pull and do not update automatically in saved report results. Download your reports every Monday to maintain a historical archive.
Use Email Notifications: Because processing times vary (up to 70 minutes), always select “Notify via Email when complete” during report creation.
Leverage the Glossary: If you cannot find a specific metric, hover over the attribute in Report Builder to see its definition and its DSS alias.
Name Reports Strategically: Use titles that reflect your filters (e.g., “Store Sales_6Month_WMCalendar”) to easily distinguish between multiple active reports.
Inventory replenishment is important for optimizing stock levels, avoiding stockouts or excess inventory, and supporting business growth. Having effective strategies to manage inventory ensures shelves remain stocked, helps prevent lost sales, and minimizes excess inventory costs. On demand replenishment, a restocking strategy driven by customer demand, allows for efficient inventory control and can reduce shipping costs by minimizing split shipments.
Overcoming Common Challenges in Walmart Replenishment
Navigating Walmart’s replenishment process requires more than just keeping shelves stocked—it demands a strategic approach to inventory management that aligns with ever-changing customer demand and supply chain dynamics. Many suppliers encounter persistent challenges that can disrupt the stock replenishment process and impact their ability to meet customer demand.
Inaccurate Demand Forecasting
One of the most significant hurdles is predicting customer demand with precision. Fluctuations in consumer behavior, seasonal peaks, and unexpected market trends can all lead to discrepancies between forecasted and actual sales. Inaccurate demand forecasting often results in either excess inventory or stockouts, both of which increase inventory costs and risk lost sales.
Solution: Leverage advanced inventory management software and real-time sales data to refine your demand forecasting models. Incorporate historical data, ongoing trends, and up-to-date information from multiple sales channels to create a more accurate picture of forecasted demand.
Maintaining Optimal Inventory Levels
Striking the right balance between too much and too little inventory is a constant challenge. Overstocking ties up capital and increases storage costs, while understocking leads to lost sales and unhappy customers.
Solution: Implement a robust inventory replenishment process that includes continuous monitoring of inventory levels, safety stock calculations, and automated reorder point triggers. Using an inventory management system that integrates with Walmart’s data sources can help ensure you always have the optimal quantities on hand to fulfill customer orders.
Supply Chain Disruptions
Delays from manufacturers and partners, transportation issues, or distribution center bottlenecks can all disrupt the replenishment process. These disruptions can quickly lead to out-of-stocks, especially for high demand items.
Solution: Build supply chain resilience by diversifying business partners, monitoring partner performance, and maintaining clear communication with Walmart’s Replenishment Management Teams. Regularly review your supply chain for potential vulnerabilities and develop contingency plans to minimize the impact of disruptions.
Managing Lost Sales and Customer Satisfaction
Every out-of-stock situation represents a missed opportunity to meet customer demand and can erode customer satisfaction. Lost sales not only affect immediate revenue but can also damage long-term brand loyalty.
Solution: Adopt effective inventory replenishment strategies that prioritize high demand items and use real time tracking to identify and address low stock levels before they result in lost sales. Regularly analyze sales data and adjust your replenishment process to align with changing market conditions and consumer demand.
By proactively addressing these common challenges, suppliers can optimize their inventory management, reduce costs, and ensure they consistently meet Walmart’s expectations for product availability. Effective inventory replenishment is not just about moving inventory—it’s about building a resilient supply chain that adapts to ongoing trends and delivers on customer promise.
Utilizing a Third-Party Inventory Management Software Solution
Navigating the manual report-building process in Scintilla can be time-consuming for large categories. Automated systems, such as robotics and automated storage solutions, can improve the speed, accuracy, and efficiency of inventory replenishment processes.
Automation also reduces human error in data handling and replenishment, resulting in more accurate stock levels and smoother operations. Automated inventory management software can streamline the stock replenishment process, saving time and reducing the risk of out-of-stock fines.
Third-party software solutions like Analytics with SPS Commerce provide automated weekly reports, pulling your GRS and POS forecasts directly without the manual steps in the app. SPS Commerce aggregates the sales and POS data from all of your trading partners into one place, so you don’t have to be dependent on a wide variety of unwieldy retailer portals.
