Abstract
Given uncertain popularity of new products by location, fast fashion retailer Zara faces a tradeoff. Large initial shipments to stores reduce lost sales in the critical first days of the product life cycle, but maintaining stock at the warehouse allows restocking flexibility once initial sales are observed. In collaboration with Zara, we develop and test a decision support system featuring a data-driven model of forecast updating and a dynamic optimization formulation for allocating limited stock by location over time. A controlled field experiment run worldwide with 34 articles during the 2012 season showed an increase in total average season sales by approximately 2% and a reduction in the number of unsold units at the end of the regular selling season by approximately 4%.
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