Out of Stock Product Recommender: Solving the Peanut Butter Problem


This paper lays out a framework for substituting out-of-stock items from the perspective of a retail store or order fulfillment platform. We first define the problem from a business perspective and argue for how this solution helps a store’s bottom line. Then we propose a design for building a recommendation engine that uses available data to estimate a percent match between all products within a store. Finally, we propose an architecture for hosting the product data and recommender using a modern data warehouse.