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 an economy where consumer expectations are rapidly evolving, suppliers must leverage every available resource to optimize their operations and drive business growth. By effectively utilizing store fulfillment data, suppliers may gain valuable insights into inventory management, sales performance, and customer behavior. This article explores how suppliers can use this data to help inform business decisions and potentially enhance operations.
What is Store Fulfillment?
Store Fulfillment, also known as Online Pickup & Delivery (OPD), refers specifically to how online orders are picked and filled. These orders can be fulfilled through various methods, including in-store pickup, curbside pickup, express delivery ), or scheduled delivery.
The store fulfillment table provides comprehensive metrics that help suppliers understand the efficiency and accuracy of their order fulfillment processes. This data includes key performance indicators such as First Time Pick Rate, Nil Picks, Pre Substitutes, Post Substitutes, Scheduled Nil Picks, and Unscheduled Nil Picks.
First Time Pick Rate (FTPR): The percentage of items that were picked in the first pick-walk for store fulfilled orders.
Nil Pick: A measure of the units not picked or substituted by a store. In general, Nil Pick is an event of not being able to pick an ordered item, substitute an item, or provide a manager override since it is not available on the current shelf.
Pre Substitutes: Measures the sum of items picked as ordered and manager approved overrides divided by the order quantity requested to pick.
Post Substitutes: Substitutions made after the initial pick attempt has failed, often based on real-time inventory checks.
Scheduled Nil Picks: Items that were not fulfilled for scheduled orders.
Unscheduled Nil Picks: Units not picked or substituted by a store for unscheduled orders.
By analyzing these metrics, suppliers can gain insights into how well their products are being picked and filled, which can inform their inventory management and operational strategies.
What are the Benefits of Store Fulfillment (OPD) Data?
Store fulfillment data provides numerous benefits to suppliers:
Improved Inventory Management: Accurate data on inventory levels, turnover rates, and out-of-stocks may help suppliers maintain optimal stock levels and reduce the risk of overstocking or stockouts.
Enhanced Demand Forecasting: Analyzing sales trends and customer preferences could help suppliers better predict demand and adjust their production and distribution strategies accordingly.
Informed Decision-Making: Data-driven insights enable suppliers to make informed decisions about product placement, pricing, promotions, and replenishment.
Performance Monitoring: By tracking key metrics such as First Time Pick Rate (FTPR), Pre Substitute Rate, Post Substitute Rate, and Nil Picks, suppliers can monitor their performance and identify areas for improvement.
Key Aspects of Leveraging Store Fulfillment Data
1. Sales and Inventory Data
Access to sales and inventory data allows suppliers to monitor stock levels and sales performance directly...
2. Importance of Retail Analytics
Identifying Trends: Understanding category trends can help align products with current market demands.
Performance Analysis: Explaining fluctuations in sales can help identify successful strategies or areas needing improvement.
Buyer Engagement: Anticipating and addressing buyer questions with data-driven insights.
Customer Understanding: Gaining insights into shopper behavior across various channels to tailor offerings accordingly.
3. Inventory and Supply Chain Optimization
Leveraging store fulfillment data allows suppliers to optimize their inventory and supply
Example Use Case
Optimizing Inventory Levels: By analyzing Store Sales data alongside Store Fulfillment data, suppliers may predict future demand based on past sales trends, optimize inventory placement by region and store, identify sales fluctuations due to price changes, and reduce overstock situations by adjusting fulfillment rates accordingly.
Table Name
Technical Name
Business Name
Store Fulfillment
bus_dt
Business Date
Store Fulfillment
catlg_item_id
Catalog Item ID / eComm Prod ID
Store Fulfillment
cust_order_qty
Customer Order Quantity
Store Fulfillment
ftpr
First Time Pick Rate
Store Fulfillment
nil_pick_qty
Nil Picks
Store Fulfillment
postsub_rate
Post Substitute Rate
Store Fulfillment
presub_rate
Pre Substitute Rate
Store Fulfillment
schdl_nil_pick_rate
Scheduled Nil Pick Rate
Store Fulfillment
store_nbr
Store Number
Store Fulfillment
unschdl_nil_pick_rate
Unscheduled Nil Pick Rate
Store Fulfillment
wm_item_nbr
Walmart Item Number
Store Sales
geo_region_cd
Geographic Region Code
Store Sales
ly_sales_amt
POS Sales-Last Year
Store Sales
rpt_cd
Sales By Type
Store Sales
svc_chnl_nm
Service Channel
Store Sales
ty_sales_amt
POS Sales-This Year
Store Sales
wm_yr_wk_nbr
Walmart Year Week Number
Conclusion
Store fulfillment data is a powerful tool that suppliers can use to help optimize their operations and make informed business decisions. By understanding and leveraging key metrics, suppliers may improve inventory management, enhance demand forecasting, and drive sales growth. In today's fast-paced retail environment, the ability to effectively use store fulfillment data is essential for helping to maintain a competitive edge and delivering superior customer experience.
