Simulation-based inventory management of perishable products via linear discrete choice models

Abstract

Retail inventory management of perishable items, like fresh food, is a relevant and complex problem. It is relevant in the light of trends towards the reduction of food waste, and because of potential cross-sales interaction with other item categories. It is complex, because of multiple sources of uncertainty in supply, demand, and quality, and other complicating factors like seasonality within the week, FIFO/LIFO consumer behavior, and potential substitutions between items, possibly because of a stockout. Similar items may be vertically differentiated due to intrinsic quality, which is also related with item age, or brand image, as it could be the case when a retail chain stocks both a brand item and a private label one. In the paper, we adapt a simple discrete choice model to represent consumers’ heterogeneity and different tradeoffs between price and quality, and apply simulation-based optimization to learn simple ordering rules for two vertically differentiated items, adapted to a seasonal case, in order to maximize long-term average profit under a lost sales assumption. While well-known constant and base-stock policies need not be optimal, they are simple to communicate and apply. We explore combinations of such rules for the two items, obtaining some useful managerial insights.