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Dynamic pricing and inventory control with learning

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  • Nicholas C. Petruzzi
  • Maqbool Dada

Abstract

Optimal operating policies and corresponding managerial insight are developed for the decision problem of coordinating supply and demand when (i) both supply and demand can be influenced by the decision maker and (ii) learning is pursued. In particular, we determine optimal stocking and pricing policies over time when a given market parameter of the demand process, though fixed, initially is unknown. Because of the initially unknown market parameter, the decision maker begins the problem horizon with a subjective probability distribution associated with demand. Learning occurs as the firm monitors the market's response to its decisions and then updates its characterization of the demand function. Of primary interest is the effect of censored data since a firm's observations often are restricted to sales. We find that the first‐period optimal selling price increases with the length of the problem horizon. However, for a given problem horizon, prices can rise or fall over time, depending on how the scale parameter influences demand. Further results include the characterization of the optimal stocking quantity decision and a computationally viable algorithm. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 303–325, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10013

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  • Nicholas C. Petruzzi & Maqbool Dada, 2002. "Dynamic pricing and inventory control with learning," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(3), pages 303-325, April.
    • Handle: RePEc:wly:navres:v:49:y:2002:i:3:p:303-325
    DOI: 10.1002/nav.10013
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    3. Sergii Bogachov & Aleksy Kwilinski & Boris Miethlich & Viera Bartosova & Aleksandr Gurnak, 2020. "Artificial intelligence components and fuzzy regulators in entrepreneurship development," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 487-499, December.
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    5. Qing Li & Shaohui Zheng, 2006. "Joint Inventory Replenishment and Pricing Control for Systems with Uncertain Yield and Demand," Operations Research, INFORMS, vol. 54(4), pages 696-705, August.
    6. Yiangos Papanastasiou, 2020. "Newsvendor Decisions with Two-Sided Learning," Management Science, INFORMS, vol. 66(11), pages 5408-5426, November.
    7. Kyle Y. Lin, 2004. "A sequential dynamic pricing model and its applications," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 501-521, June.
    8. Philipp Afèche & Barış Ata, 2013. "Bayesian Dynamic Pricing in Queueing Systems with Unknown Delay Cost Characteristics," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 292-304, May.
    9. Man Yu & Laurens Debo & Roman Kapuscinski, 2016. "Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods," Management Science, INFORMS, vol. 62(2), pages 410-435, February.
    10. Victor F. Araman & René Caldentey, 2009. "Dynamic Pricing for Nonperishable Products with Demand Learning," Operations Research, INFORMS, vol. 57(5), pages 1169-1188, October.
    11. Tatsiana Levina & Yuri Levin & Jeff McGill & Mikhail Nediak, 2009. "Dynamic Pricing with Online Learning and Strategic Consumers: An Application of the Aggregating Algorithm," Operations Research, INFORMS, vol. 57(2), pages 327-341, April.
    12. Vincent C. Li & Yat-wah Wan & Chi-Leung Chu & Yi-Cheng Lin, 2020. "A Dynamic Programming-Based Heuristic for Markdown Pricing and Inventory Allocation of a Seasonal Product in a Retail Chain," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(01), pages 1-30, January.
    13. Lu Xiao & Hang Zhang & Yong Qin, 2020. "Competitive Pricing of Innovative Products with Consumers’ Social Learning," Sustainability, MDPI, vol. 12(9), pages 1-13, May.
    14. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).

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