28.10.2020

“Bang bang”: an optimal sales strategy for perishable goods

HU, Peng | SHUM, Stephen | YU, Man

Shopkeepers and store managers should base decisions on whether to discount or destroy leftover perishable goods on consumer behavior, an innovative new study has found. While marking down prices can provide welcome revenue for foodstuffs that would otherwise go to waste, this can also result in “intertemporal cannibalization”—a reduction in full-price sales as customers strategically forward buy. In such cases, an intelligent and adaptable approach to discounting and stock ordering is both pragmatic and profitable, explain HKUST’s Man Yu and co-authors.

The research was inspired by the problems faced by a local bakery chain in its day-to-day operations. Every day, the store manager needed to decide whether to discount or destroy any leftover stock, and how much to order for the following day. The choice to discount or destroy is not a trivial one, as it can affect the overall profit. “When the markdown quantity increases, the chance of selling to an additional customer at the regular price decreases,” the researchers caution.

Understanding the impact of discounting on future demand is fundamental to maximizing profits. To this end, the researchers modeled the complex and dynamic consumer environment to derive the optimal sales strategy for perishable goods. To mimic changing—and often random—patterns of consumption, the team also developed a micro-model incorporating consumers’ individual purchasing decisions.

The researchers recommend that if left with significant amounts of stock, retailers should discount it. If a small amount of stock remains at the end of the day, it should be destroyed.

This “bang bang” strategy delivers a double benefit: it can both reduce waste and maximize returns. While a bang-bang approach is likely to be optimal for all businesses that sell fresh produce, it is especially well suited to “highly competitive industries like bakeries or groceries, where freshness and quality are among the key competitive dimensions,” note the researchers.

The model proposed by Yu and the research team is flexible and responsive, using demand as the basis for discounts and future ordering decisions. A store should begin by predicting the dip in future demand for perishable food products that occurs when goods are reduced in price, the researchers advise, and “reduce the quantity ordered for sale at full price whenever it offers a discount.”

The study is an elegant approach to capacity–revenue management in a dynamic consumer environment and is the first in the world to preserve quasiconvexity in a multi-period maximization problem. Simply put, the team has developed a model complex enough to calculate the impact of strategic consumer behavior on demand and discounting across both full-price and discount sales. “To the best of our knowledge,” say the researchers, “this is the first stochastic and dynamic inventory model facing strategic customer behavior with joint ordering and discounting decisions.”

The findings provide a robust and evidence-based model for organizations and individuals faced with difficult daily decisions on whether to discount or destroy perishable stock.

YU, Man

Associate Professor
Information Systems, Business Statistics & Operations Management