Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment

Nuri Bora Keskin · Yuexing Li · Jing-Sheng Jeannette Song

103 citations0/0 files storedManagement Science

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Supply Chain and Inventory Management Consumer Market Behavior and Pricing Optimization and Search Problems

Abstract

We consider a retailer that sells a perishable product, making joint pricing and inventory ordering decisions over a finite time horizon of T periods with lost sales. Exploring a real-life data set from a leading supermarket chain, we identify several distinctive challenges faced by such a retailer that have not been jointly studied in the literature: the retailer does not have perfect information on (1) the demand-price relationship, (2) the demand noise distribution, (3) the inventory perishability rate, and (4) how the demand-price relationship changes over time. Furthermore, the demand noise distribution is nonparametric for some products but parametric for others. To tackle these challenges, we design two types of data-driven pricing and ordering (DDPO) policies for the cases of nonparametric and parametric noise distributions. Measuring performance by regret, that is, the profit loss caused by not knowing (1)–(4), we prove that the T-period regret of our DDPO policies are in the order of [Formula: see text] and [Formula: see text] in the cases of nonparametric and parametric noise distributions, respectively. These are the best achievable growth rates of regret in these settings (up to logarithmic terms). Implementing our policies in the context of the aforementioned real-life data set, we show that our approach significantly outperforms the historical decisions made by the supermarket chain. Moreover, we characterize parameter regimes that quantify the relative significance of the changing environment and product perishability. Finally, we extend our model to allow for age-dependent perishability and demand censoring and modify our policies to address these issues. This paper was accepted by David Simchi-Levi, Management Science Special Section on Data-Driven Prescriptive Analytics.

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References

2001Recent trends in modeling of deteriorating inventory 1982Perishable Inventory Theory: A Review 2012Review of inventory systems with deterioration since 2001 2005Pricing and Revenue Optimization 2015Dynamic pricing and learning: Historical origins, current research, and new directions 2010Managing Perishable and Aging Inventories: Review and Future Research Directions 2006Forecasting Time Series Subject to Multiple Structural Breaks 2014Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies

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