Fresh Food Grocer Promo Forecasting and Shrink Reduction Case Study
In brief: Case study showing how AI demand forecasting reduced perishable shrink by modeling cannibalization and promotion effects at SKU-store level.
By Alex Ibarra, Research Analyst — Planning, Order Management & Point of Sale · Supply Chain Research
Case study showing how AI demand forecasting reduced perishable shrink by modeling cannibalization and promotion effects at SKU-store level.
A leading fresh food grocer used Rubikloud's Price & Promotion Manager to address over-forecasting of perishables during promotions. The solution modeled cross-product effects including cannibalization, halo effects, and price elasticity. Results included 7% accuracy gains in key fresh categories and an estimated $39M impact from a 1.2% chain-wide accuracy improvement.
Promo cannibalization caused 25-30% losses in fresh salads and packaged meats
AI engine generates SKU-store forecasts that account for promotion timing and cross-effects
Cross-product modeling identified over-forecasting when competing brands were promoted
7% forecast accuracy lift achieved in key fresh categories
$39M estimated financial impact from 1.2% chain-wide accuracy gain
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