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Dynamic Inventory–Pricing Control Under Backorder: Demand Estimation and Policy Optimization

Author

Listed:
  • Qi Feng

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

  • Sirong Luo

    (School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China; and Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai 200433, China)

  • Dan Zhang

    (Leads School of Business, University of Colorado at Boulder, Boulder, Colorado 80309)

Abstract

Inventory-based dynamic pricing has become a common operations strategy in practice and has received considerable attention from the research community. From an implementation perspective, it is desirable to design a simple policy like a base-stock list-price (BSLP) policy. The existing research on this problem often imposes restrictive conditions to ensure the optimality of a BSLP policy, which limits its applicability in practice. In this paper, we analyze the dynamic inventory and pricing control problem in which the demand follows a generalized additive model (GAM). The GAM overcomes the limitations of several demand models commonly used in the literature, but introduces analytical challenges in analyzing the dynamic program. Via a variable transformation approach, we identify a new set of technical conditions under which a BSLP policy is optimal. These conditions are easy to verify because they depend only on the location and scale parameters of demand as functions of price and are independent of the cost parameters or the distribution of the random demand component. Moreover, although a BSLP policy is optimal under these conditions, the optimal price may not be monotone decreasing in the inventory level. We further demonstrate our results by applying a constrained maximum likelihood estimation procedure to simultaneously estimate the demand function and verify the optimality of a BSLP policy on a retail data set.

Suggested Citation

  • Qi Feng & Sirong Luo & Dan Zhang, 2014. "Dynamic Inventory–Pricing Control Under Backorder: Demand Estimation and Policy Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 149-160, February.
    • Handle: RePEc:inm:ormsom:v:16:y:2014:i:1:p:149-160
    DOI: 10.1287/msom.2013.0459
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    References listed on IDEAS

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    Cited by:

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    3. Xiting Gong & Youhua (Frank) Chen & Quan Yuan, 2022. "Coordinating Inventory and Pricing Decisions Under Total Minimum Commitment Contracts," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 511-528, February.
    4. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
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    6. Qi Feng & J. George Shanthikumar, 2018. "Supply and Demand Functions in Inventory Models," Operations Research, INFORMS, vol. 66(1), pages 77-91, 1-2.
    7. Sirong Luo & Jianrong Wang, 2017. "A technical note on the dynamic nonstationary inventory-pricing control model with lost sale," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5816-5825, October.
    8. Qi Feng & Sirong Luo & J. George Shanthikumar, 2020. "Integrating Dynamic Pricing with Inventory Decisions Under Lost Sales," Management Science, INFORMS, vol. 66(5), pages 2232-2247, May.
    9. Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.
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    11. Qi Feng & J. George Shanthikumar, 2022. "Applications of Stochastic Orders and Stochastic Functions in Inventory and Pricing Problems," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1433-1453, April.

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