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

Publication ,  Journal Article
Bora Keskin, N; Li, Y; Song, JS
Published in: Management Science
March 1, 2022

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 T2=3(logT)1=2 and T1=2logT 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.

Duke Scholars

Published In

Management Science

DOI

EISSN

1526-5501

ISSN

0025-1909

Publication Date

March 1, 2022

Volume

68

Issue

3

Start / End Page

1938 / 1958

Related Subject Headings

  • Operations Research
  • 46 Information and computing sciences
  • 38 Economics
  • 35 Commerce, management, tourism and services
  • 15 Commerce, Management, Tourism and Services
  • 08 Information and Computing Sciences
 

Citation

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MLA
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Bora Keskin, N., Li, Y., & Song, J. S. (2022). Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science, 68(3), 1938–1958. https://doi.org/10.1287/mnsc.2021.4011
Bora Keskin, N., Y. Li, and J. S. Song. “Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment.” Management Science 68, no. 3 (March 1, 2022): 1938–58. https://doi.org/10.1287/mnsc.2021.4011.
Bora Keskin N, Li Y, Song JS. Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science. 2022 Mar 1;68(3):1938–58.
Bora Keskin, N., et al. “Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment.” Management Science, vol. 68, no. 3, Mar. 2022, pp. 1938–58. Scopus, doi:10.1287/mnsc.2021.4011.
Bora Keskin N, Li Y, Song JS. Data-Driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment. Management Science. 2022 Mar 1;68(3):1938–1958.

Published In

Management Science

DOI

EISSN

1526-5501

ISSN

0025-1909

Publication Date

March 1, 2022

Volume

68

Issue

3

Start / End Page

1938 / 1958

Related Subject Headings

  • Operations Research
  • 46 Information and computing sciences
  • 38 Economics
  • 35 Commerce, management, tourism and services
  • 15 Commerce, Management, Tourism and Services
  • 08 Information and Computing Sciences