The challenge

Desigual manages a highly varied product assortment, with sell-through differing significantly between stores. Product managers were spending up to three hours each week analysing historical sales in Excel to decide where inventory should move. The process relied on the previous 7–15 weeks of sales and lacked forecasting at style, colour and size level across more than 700 style-colour combinations.

The solution

An AI-powered inventory forecasting and rebalancing system was introduced across company-owned stores in five European markets. It forecast future demand at item and size level and generated store-to-store transfer recommendations based on where products were expected to sell, rather than relying only on historical performance. This replaced much of the manual analysis previously required from product managers.

Practical value

The system generated more than 215,000 inventory rebalancing recommendations and delivered a 12% revenue lift from transfers during the test quarter.

Product managers also saved more than three hours of manual analysis per week per person, while better inventory positioning helped reduce stockout risk and improve cash flow.

The case demonstrates the commercial value of moving from historical analysis towards forward-looking decision support.

The broader lesson is that AI creates value when it helps experienced teams make more precise decisions at a scale that would be difficult to manage manually.