European retailer cuts €2M in operating costs in 90 days with AI SKU rationalization
Using ThroughPut AI, a 65-year-old European retailer reprioritized 150 high-value SKUs and trimmed 200 low-demand items, reducing operating expenses by €2 million within 90 days and identifying up to €10 million in bottom-line impact per facility.
Background
The retailer operated in a low-margin industry (typically 1–3% margins) with volatile demand and lacked a data-driven, end-to-end view of demand. It needed to identify and reduce low-value, low-demand SKUs to free capacity for higher-value products, reduce material/operational costs, and lower logistics costs through better OTIF planning.
What Was Implemented
- ThroughPut AI demand-sensing applied to time-stamped point-of-sale data, integrated with existing systems (no new data sources or infrastructure overhaul).
- Automated SKU segmentation by value and demand into a demand-value matrix.
- Real-time product-mix optimization, dead-stock elimination, and replenishment/allocation optimization.
- Prioritized the top ~150 high-value SKUs and flagged ~200 low-demand, poor-OTIF SKUs for trimming.
Results
The retailer reduced operating expenses by €2 million through better allocation of the top 150 SKUs , identified ~200 low-demand SKUs for trimming, and reached measurable ROI within 90 days . ThroughPut reports an identified opportunity of up to €10 million in bottom-line impact per facility and frames the broader margin opportunity as up to €30 million .
Lessons
- AI SKU rationalization can deliver fast, measurable ROI (within ~90 days) by working from existing POS data.
- Segmenting SKUs by both demand and value — not demand alone — pinpoints what to grow vs. trim.
- Trimming low-OTIF, low-demand SKUs lowers operating and logistics costs while freeing capacity for higher-value products.
- Vendor-reported single-client results should be read as directional and self-reported.