Case Study

1.5-2% Sales Improvement through Store x Item x Day Level Demand Forecasting for Grocery Retail

Business Objective

Our client is a leading US-based grocery retailer with 100+ categories and 10,000 + SKU’s. The current inventory planning process for promo and non-promo time periods relied heavily on business rules developed over time. There was a need to revisit those and develop predictive models in order to have a robust demand forecasting process that accurately accounts for promotional impact.


  • Building a full-blown forecasting solution at an Item x Store x Day level
  • Forecasting for recently introduced products and brand new products

Solution Methodology 

  • Collected sales and promotional data. Blended them to have a uniform Item x Store x Day level granularity.
  • Pre-processed to de-trend, de-seasonalize, and remove holiday effects
  • Built models for baseline and promotional lift estimation, using a combination of time series and frequency-based approach (for sparse data).
  • Developed Sub-Category/Category models for SKUs that didn’t meet range (# of stores) or time period criteria
  • Used store level daily sales distribution indices to break down weekly to daily sales
  • Identified similar products for new products by clustering for 10+ features (like sales, regional preference, complement, substitute, promoted group, etc.) and modeled based on them.
  • Automated end-to-end process and enabled calibration for daily/weekly refresh.

Business Impact

  • Solution tested against current approach: yielded item x store x day level forecast accuracy improvements ranging from 5% to 80%. Weighted average improvement was around 15%.
  • Estimated business value due to stock-out avoidance and improved service levels was around 1.5% to 2% on sales.
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