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Periodic‐review inventory models with inventory‐level‐dependent demand

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  • Yigal Gerchak
  • Yunzeng Wang

Abstract

Demand for some items can depend on the inventory level on display, a phenomenon often exploited by marketing researchers and practitioners. The implications of this phenomenon have received scant attention in the context of periodic‐review inventory control models. We develop an approach to model periodic‐review production/inventory problems where the demand in any period depends randomly, in a very general form, on the starting inventory level. We first obtain a complete analytical solution for a single‐period model. We then investigate two multiperiod models, one with lost sales and the other with backlogging, whose optimal policies turn out to be myopic. Some extensions are also discussed. © 1994 John Wiley & Sons, Inc.

Suggested Citation

  • Yigal Gerchak & Yunzeng Wang, 1994. "Periodic‐review inventory models with inventory‐level‐dependent demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 99-116, February.
    • Handle: RePEc:wly:navres:v:41:y:1994:i:1:p:99-116
    DOI: 10.1002/1520-6750(199402)41:13.0.CO;2-W
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    References listed on IDEAS

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    1. Mordechai Henig & Yigal Gerchak, 1990. "The Structure of Periodic Review Policies in the Presence of Random Yield," Operations Research, INFORMS, vol. 38(4), pages 634-643, August.
    2. Marcel Corstjens & Peter Doyle, 1981. "A Model for Optimizing Retail Space Allocations," Management Science, INFORMS, vol. 27(7), pages 822-833, July.
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    Cited by:

    1. Oded Berman & David Perry & Wolfgang Stadje, 2007. "Performance Analysis of a Fluid Production/Inventory Model with State-dependence," Methodology and Computing in Applied Probability, Springer, vol. 9(4), pages 465-481, December.
    2. Mohammad Saffari, 2022. "Periodic inventory management when demand stochastically depends on shelf-stock," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1489-1501, December.
    3. Prashant Chintapalli & Jishnu Hazra, 2015. "Pricing and inventory management during new product introduction when shortage creates hype," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 304-320, June.
    4. Yan, Xiaoming & Chao, Xiuli & Lu, Ye, 2024. "Optimal control policies for dynamic inventory systems with service level dependent demand," European Journal of Operational Research, Elsevier, vol. 314(3), pages 935-949.
    5. Nan Yang & Renyu Zhang, 2014. "Dynamic Pricing and Inventory Management Under Inventory-Dependent Demand," Operations Research, INFORMS, vol. 62(5), pages 1077-1094, October.
    6. Amar Sapra & Van-Anh Truong & Rachel Q. Zhang, 2010. "How Much Demand Should Be Fulfilled?," Operations Research, INFORMS, vol. 58(3), pages 719-733, June.
    7. Zhang, Jie & Xie, Weijun & Sarin, Subhash C., 2021. "Robust multi-product newsvendor model with uncertain demand and substitution," European Journal of Operational Research, Elsevier, vol. 293(1), pages 190-202.
    8. Chuang, Chia-Hung & Zhao, Yabing, 2019. "Demand stimulation in finished-goods inventory management: Empirical evidence from General Motors dealerships," International Journal of Production Economics, Elsevier, vol. 208(C), pages 208-220.
    9. Youhua (Frank) Chen & Ye Lu & Minghui Xu, 2012. "Optimal inventory control policy for periodic‐review inventory systems with inventory‐level‐dependent demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(6), pages 430-440, September.
    10. Srinivas Bollapragada & Thomas E. Morton, 1999. "Myopic Heuristics for the Random Yield Problem," Operations Research, INFORMS, vol. 47(5), pages 713-722, October.
    11. Stephen A. Smith & Narendra Agrawal, 2017. "Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 290-304, May.

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