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

55 Pages Posted: 18 Jun 2019 Last revised: 4 Sep 2020

See all articles by N. Bora Keskin

N. Bora Keskin

Duke University - Fuqua School of Business

Yuexing Li

Duke University, Fuqua School of Business, Students

Jing-Sheng Jeannette Song

Duke University - Fuqua School of Business

Date Written: August 7, 2020

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, i.e., 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 T^{2/3}(logT)^{1/2} and T^{1/2}logT 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.

Keywords: dynamic pricing, inventory control, perishable inventory, nonstationary environment, data-driven analysis, estimation, exploration-exploitation

Suggested Citation

Keskin, N. Bora and Li, Yuexing and Song, Jing-Sheng Jeannette, Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment (August 7, 2020). Available at SSRN: https://ssrn.com/abstract=3401188 or http://dx.doi.org/10.2139/ssrn.3401188

N. Bora Keskin (Contact Author)

Duke University - Fuqua School of Business ( email )

100 Fuqua Drive
Durham, NC 27708-0120
United States

HOME PAGE: http://faculty.fuqua.duke.edu/~nk145/

Yuexing Li

Duke University, Fuqua School of Business, Students ( email )

Durham, NC
United States

Jing-Sheng Jeannette Song

Duke University - Fuqua School of Business ( email )

100 Fuqua Drive
Duke University
Durham, NC 27708
United States

HOME PAGE: http://people.duke.edu/~jssong/

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