Challenge
A fresh retail business needed to optimize replenishment and inventory management for perishable products to reduce spoilage, improve shelf freshness, and increase sales.
Optimizing inventory management of perishable products

A fresh retail business needed to optimize replenishment and inventory management for perishable products to reduce spoilage, improve shelf freshness, and increase sales.
Implemented an AI-driven replenishment approach combining demand forecasting, optimized ordering, and KPI-driven inventory management for perishable products.
Reduced inventory costs by €1.6M while improving forecast accuracy, replenishment performance, and scaling the approach across 86% of fresh product categories.
Being the spoilage cost reduction one of the main concerns of our customer’s fresh retail business, the increase of the replenishment efficiency was set as a strategic priority.
The combination of a replenishment method that lacked several key fresh-retail specificities, the stores’ manual orders that were significantly exceeding the ideal stock levels, an inaccurate forecasting process and obsolete management tools were contributing to product spoilage.
The project goal was to increase sales throw the promotion of freshness on the shelves, reducing food spoilage rates thanks to the optimization inventory management of perishable products.
The ROP methodology proposed a solution based on three main topics:
With the overall decrease of stock levels, there was a significant shrinkage (1,4 M€ in Fruits & Vegetables) and inventory costs reduction (0,2 M€ in Cured Meat & Cheese).
In some cases, it was also possible to reduce stock-outs, due to a more accurate forecast.
The main intangible benefit was the overall replenishment process control by the stock management team, now provided with the right tools and the suitable know-how.
The methodology is currently being implemented at the whole fresh department, now with 86% of all categories fully covered by ROP.