Assortment Optimization with Replacement Options for Retail Platforms with Stockout Risk

A Smarter Way to Plan for Stockouts Online retail platforms increasingly face a basic operational problem: a product that appears available when a customer places an order may be out of stock when the order is fulfilled. This paper shows how platforms can respond more effectively by optimizing not only the set of products shown to customers, but also the replacement options offered when stockouts occur. The authors study both non-adaptive and adaptive approaches, where the latter tailors replacement choices to the customer’s initially selected item. They show that both problems are computationally challenging, but develop approximation algorithms with strong performance guarantees. Using Instacart data, they find that explicitly modeling replacement options improves expected revenue, and that adaptive replacement assortments provide additional gains, especially in categories with higher stockout risk. This news story is based on the accepted paper by Mitrofanov, Topaloglu, and Wang, “Assortment Optimization with Replacement Options for Retail Platforms with Stockout Risk”.