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

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.

Automated summary

Retail inventory management of perishable items, such as fresh food, is a complex and relevant problem due to the reduction of food waste and potential sales interaction with other item categories. Multiple factors contribute to the complexity, including supply, demand, quality uncertainty, seasonality, consumer behavior, and item substitutions. This study adapts a discrete choice model to represent consumer heterogeneity and uses simulation-based optimization to learn simple ordering rules for two vertically differentiated items to maximize long-term average profit.