Slow-Moving and Obsolete Inventory Detection

A large international fashion retailer operating more than 270 stores across 10 countries implemented AI-powered allocation, replenishment, transfer, and markdown optimization across an 8,000-SKU catalog. According to a 2025 case study published by the solution provider, the retailer reduced lost sales by 6% by optimizing inventory availability and improving allocation accuracy. Inventory productivity improved through more efficient inter-store transfers, while markdown losses declined through lifecycle-based discounting strategies that matched price reductions to demand elasticity at the individual item level. The implementation moved the retailer from manual, reactive inventory decisions to automated, AI-driven planning within a single deployment cycle.

In a separate implementation, a multinational apparel group operating more than 2,300 stores and 85 brands across 14 countries deployed AI-driven markdown and transfer optimization to address chronic excess inventory challenges. The system analyzed price elasticity, seasonality, inventory levels, and consumer demand trends to determine optimal markdown depth, timing, and frequency for each product. The retailer reported lower markdown losses, increased sell-through rates, and higher margins, with the solution deployed in approximately three months. A European specialty packaging company achieved an 8.5% reduction in warehouse capital and 11% improvement in on-time deliveries after implementing AI-powered demand forecasting and inventory optimization, according to a published case study by Kortical.