Store Fulfillment (OPD)
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 data feed 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.
