An Empirical Analysis of Intra‐Firm Product Substitutability in Fashion Retailing

This study offers an empirical investigation of inventory and sales dynamics in a large‐scale retail network setting. We infer the impact of product shortages on sales in neighboring outlets using unique data from a large fast fashion retailing chain and an Instrumented Difference‐in‐Differences (DDIV) methodology. Our analysis reveals that sales for a particular item at a focal store increases when that same item experiences stock‐outs in neighboring stores. Our empirical findings suggest that there is substitutability across stores, and that this substitutability is the strongest in the period when the stock‐out is observed for the first time, and decreases as time passes following the stock‐out. In order to assess the implications of considering the impact of stock‐outs on inventory allocation, we develop an optimization model that is calibrated using parameters estimated via our earlier DDIV analysis. The simulation analysis confirms that revenues markedly improve on average by 6% under low demand variance and by 14% under high demand variance when neighboring stock‐out information is taken into account for sales forecasting while optimizing initial inventory allocations. Finally, we conduct sensitivity analysis to evaluate how these potential revenue improvements vary with turnover, product price, and inventory.