Dynamic expiration date-based discounting of fresh food products

Abstract

To reduce food waste, many supermarkets discount food products that are close to their expiration date. In practice, this is done either by discount labels put on the product or by electronic shelf labels (or digital price tags) showing the price per expiration date. Digital price tags allow to easily change the price of products and to apply different discount rates to items with different expiration dates. An important question to practitioners is when and how much discount to offer. In this study, we use Stochastic Dynamic Programming (SDP) to derive optimal expiration-date-based discounting policies for a profit-maximizing retailer who sells a product with $m$ periods (e.g., days) of shelf life. We compare various discounting strategies, such as static last-day discounting, optimal dynamic last-day, and last-two-days discounting, against the no-discounting strategy.

The model allows products of different expiration dates to be in stock simultaneously, as replenishment happens every period. In the last-day discounting policies, two selling prices co-exist: the regular price and the discounted price. When applying a last-two-days discounting policy, three selling prices co-exist. Demand and product withdrawal depend on both price and product age (freshness). We consider different customer picking behavior, and divide customers into First-Expiry-First-Out (FEFO) and Last-Expiry-First-Out (LEFO) consumers (i.e, customers that pick the oldest items first and customers that take the freshest items available). For LEFO customers, we also consider that a fraction of these customers will pick discounted old items (depending on the size of discount). Finally, extra demand is attracted as long as discounted products are available.

Optimal policies are derived by SDP and evaluated by simulation to generate insights into the impact of discounting on profits, sales, fill rates, and waste. Various key factors, such as shelf life, customer picking behavior, and discount sensitivity are analyzed in detail. The results show that the last-two-days discounting policy performs well. Averaged over all experiments, this policy demonstrates a 3.8% increase in profits compared to no-discounting, and a waste reduction from 5.6% to 3.6%. Smaller, but still significant improvements are shown over simpler discounting policies.