SBU & Department 46 Hierarchy Changes:
Fashion → Style;
Home → Style;
ETS + Hardlines → ETS/Hardlines;
Department 46 (Beauty) → Style.
Important Dates:
Operational hierarchy updates: October 20; Shopper Behavior updates: December 7. Please login to
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We are aware of incorrect data for the street_dt field in Product Dimensions. Our data teams are prioritizing identifying a resolution. We anticipate that the corrected data will be available in the September 25, 2026 refresh. We apologize for the inconvenience.
We are aware of incorrect data for the street_dt field in Product Dimensions. Our data teams are prioritizing identifying a resolution. We anticipate that the corrected data will be available in the September 25, 2026 refresh. We apologize for the inconvenience.
As of today, September 23, Scintilla’s Store Inventory metrics have transitioned from a legacy data source to the new Enterprise Inventory (EI) data source, meaning that any data for September 23 and onwards will sourced from EI. This aligns Scintilla with the enterprise-standard inventory system, eliminates inconsistent logic within the legacy data source, and improves replenishment metric accuracy. Merchandising already completed this transition for Merch One on Tuesday, June 23. Historical data (June 1, 2025 – September 22, 2026) will go through a restatement process. Restatement work will begin on September 24, 2026 and will not be completed until October 8, 2026. Approximately 60 days' worth of data will be restated daily (with no restatement work performed on Fridays and Saturdays). In completing the historical restatement on October 8, we will be eliminating the temporary variance that existed between Scintilla and Merch One from June 23–September 22, 2026. During restatement period, clients should expect fluctuations to the relevant metrics that will be resolved once we finalize on October 8. They should also expect fluctuations due to changes to logic / formulas for the following fields: Instock % - TY/LY, Traited & Valid Store Count, Traited Item count and related velocity metrics. The following columns will be removed in the UI and null in BI Link from the date of release: Carry opt Cd, Carry Opt Status.
Due to an ongoing issue, the availability of data assets is currently delayed. Our team is actively working to resolve the issue and restore access as quickly as possible. We appreciate your patience and understanding.
On September 18, users may experience delays in receiving Store Inventory (catman) data, potentially resulting in an SLA miss. We are aware of the delay and our team is working to resolve it. We apologize for the inconvenience.
Hierarchy changes are now reflected across the Data Access and Collaboration portfolio. If you have any pre-built reports utilizing hierarchy filters, you may need to clear these filters and recreate them with the new hierarchies.
Please submit requests via a support ticket to get historical UPC Sales to reflect hierarchy changes in Shopper Behavior. You can submit a ticket for these August changes beginning on Monday, August 24.
Hierarchy changes are now reflected across the Data Access and Collaboration portfolio. If you have any pre-built reports utilizing hierarchy filters, you may need to clear these filters and recreate them with the new hierarchies.
Please submit requests via a support ticket to get historical UPC Sales to reflect hierarchy changes in Shopper Behavior. You can submit a ticket for these August changes beginning on Monday, August 24.
Hierarchy changes are now reflected across the Data Access and Collaboration portfolio.
Please submit requests via a support ticket to get the updated item hierarchy. You can submit a ticket for these August changes on or after Tuesday, August 11.
We will restate corrected Markup Markdown Board data this weekend. The restated records will flow through incremental updates to both Data Feeds and Cloud Feeds, which may update values previously received. Please ensure downstream processes are prepared to consume these incrementals. We will update this notice when the restatement is complete.
We are aware of a data issue with RFID Tag Part table. We are working to fix the issue.
Service Advisory: Cloud Feeds, Data feeds Pipeline Delay We are currently experiencing an infrastructure issue impacting Cloud Feeds and Data feeds data pipelines due to a constraint within our platform. As a result, there may be delays in data availability and refreshes. We are actively engaging and working toward resolution. We will provide updates as additional information becomes available.
We have become aware that there is missing store sales data for May 26, 2026. This missing data is impacting Scintilla suppliers and the category advisor persona who rely on headless access (Cloud Feeds, Data Feeds API and Scintilla Media Data Feed). Our team investigated the issue and resolved it as of 11:30 am CT this morning. Report Builder is unaffected and has all sales data for May 26, 2026. Please see the next steps below on how to restore missing sales data. Data Feeds API: The historical file for the affected May 26 store sales data will be made available, and suppliers will need to manually consume it to recover the missing records.
We have become aware that there is missing store sales data for May 26, 2026. This missing data is impacting Scintilla suppliers and the category advisor persona who rely on headless access (Cloud Feeds, Data Feeds API and Scintilla Media Data Feed). Our team investigated the issue and resolved it as of 11:30 am CT this morning. Report Builder is unaffected and has all sales data for May 26, 2026. Please see the next steps below on how to restore missing sales data. Cloud Feeds: The data will be restated and published as an on-demand update in the status table. Suppliers can manually use the status timestamp to read the restated store sales data using CDF. If they do not take manual action today, then it will process automatically tomorrow via the normal CDF process.
In the fast-paced world of retail, accurate demand forecasting is a key component for maintaining a smooth supply chain and ensuring that store shelves are adequately stocked. Long Range Store Demand Forecast (LRSDF) is strategic data that suppliers may leverage to enhance their inventory, production, and logistics planning months in advance.
What is Long Range Store Demand Forecast?
A demand forecast is a projection of what Walmart customers are expected to buy. It represents how much Walmart is projected to sell and when it is expected to sell. It offers a pure, unconstrained view of customer demand, agnostic of any other factors.
Long Range Store Demand Forecast (LRSDF) can be used as predictive data to project future demand for products at the store level. This forecast incorporates historical sales data, seasonal trends, regional preferences, and other relevant factors to provide a comprehensive view of the expected demand over an extended period.
Why Does LRSDF Matter?
Forecasts that are too high or too low can lead to inventory problems and ordering inconsistencies.
When the demand forecast is too high it can lead to:
An excess in inventory.
A disrupted inventory flow to shelf, which could lead to stores having an abundance of stock that may lead to items getting lost in the backroom. This can lead to missed sales opportunities .
Erratic ordering due to skipped order cycles while waiting for the excess inventory to sell through.
When the forecast is too low, it can lead to:
Out of stocks and nil picks, which could lead to lost sales.
Other items to be plugged into the wrong position or spread on the shelf, which could lead to overall modular integrity issues.
Erratic ordering, requiring high orders to make up for excessive inventory outages.
LRSDF is a significant asset, offering insights that can inform various aspects of operations. By understanding future demand trends, suppliers can better align their activities with Walmart’s needs, potentially leading to more efficient and cost-effective business practices.
What Are the Benefits of LRSDF Data?
The following are some of the potential benefits of Long Range Store Demand Forecast.
1. Demand Planning & Production Efficiency
Visibility into future demand: LRSDF provides insights into anticipated demand at the store level, enabling suppliers to adjust their production schedules accordingly.
Reduced risk of overproduction or stock shortages: By aligning production with forecasted demand, suppliers may be able to minimize excess inventory or out of stock.
2. Inventory & Supply Chain Optimization
Optimized raw material procurement: Knowing projected demand could help suppliers plan their raw material purchases more effectively.
Minimized last-minute rush orders: Accurate demand forecasting could help reduce the need for urgent orders, which often disrupt supply chains.
3. Financial Planning & Cost Management
Revenue and cash flow forecasting: LRSDF assists suppliers in predicting future revenue streams and managing cash flow based on expected orders .
Cost management: By anticipating demand, suppliers may reduce carrying costs for unsold inventory and avoid expensive expedited shipping due to unexpected demand surges.
4. Regional & Seasonal Planning
Adjusting for seasonality and regional trends: LRSDF allows suppliers to prepare for seasonal demand spikes and regional preferences.
Example Use Case
Regional Preference Forecasting
Regional preference forecasting involves tailoring inventory to meet the specific preferences and needs of different geographic areas. Here's how suppliers can use LRSDF data for this purpose:
Steps for Regional Preference Forecasting:
Geographic Analysis: Identify regional trends and preferences by analyzing sales data for different geographic regions.
Product Customization: Customize product offerings based on regional tastes and preferences.
Targeted Inventory Distribution: Allocate more inventory to regions where certain products are more popular.
Localized Promotions: Implement regional marketing campaigns to boost sales of preferred products in specific areas.
Feedback Loop: Continuously collect and analyze sales data to refine regional forecasts and adjust strategies accordingly.
Table Name
Technical Name
Business Name
Long Range Store Demand Forecast
fcst_wm_yr_wk_nbr
Forecast Walmart Year Week Number
Long Range Store Demand Forecast
final_fcst_each_qty
Final Forecast Each Quantity
Long Range Store Demand Forecast
geo_region_cd
Geographic Region Code
Long Range Store Demand Forecast
op_cmpny_cd
Operational Company Code
Long Range Store Demand Forecast
store_nbr
Store Number
Long Range Store Demand Forecast
wm_item_nbr
Walmart Item Number
Long Range Store Demand Forecast
wm_yr_wk_nbr
Walmart Year Week Number
Conclusion
Long Range Store Demand Forecast (LRSDF) serves as a valuable tool for suppliers, offering insights that may lead to more informed decision-making across production, inventory management, and supply chain operations. By leveraging LRSDF data, suppliers can potentially enhance their efficiency, reduce costs, and improve customer satisfaction.
