Dead Stock Liquidation Recommendation

A large European apparel retailer operating more than 400 stores across 20 countries deployed an AI-powered markdown optimization solution to replace manual, spreadsheet-based clearance processes. The implementation, completed within three months, used machine learning models to estimate price elasticity at the individual product level and determine optimal markdown depth, timing, and frequency for each SKU by store location. According to a 2025 case study published by invent.ai, the retailer increased clearance revenue by 6.9% and overall revenue by 2.4% while reducing markdown loss by two percentage points. The system accounted for diminishing markdown impact over time and applied distinct strategies by product category, using a clearance approach for end-of-season items and a mark-down-as-you-go strategy for underperforming mid-season products.

In a separate deployment reported by Supply and Demand Chain Executive, a North American fashion retailer piloted AI-driven clearance pricing recommendations at the store and channel level. Over a four-month pilot period, the retailer observed an 80% increase in week-to-week sales for pilot product codes, while non-pilot codes experienced a 28% decline over the same period. Inventory turns improved by 250% without sacrificing margins. The retailer subsequently expanded the solution across all product categories. A third example involves a leading sportswear and lifestyle brand that implemented algorithm-based inter-store transfers to rebalance dead stock across locations, achieving a 10% improvement in inventory health, as documented by Increff in 2025. These cases illustrate that measurable results typically emerge within one to two selling seasons, though full optimization requires ongoing model refinement and organizational adoption.