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Pooling and Dependence of Demand and Yield in Multiple-Location Inventory Systems

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  • Ho-Yin Mak

    (Department of Industrial Engineering and Logistics Management, The Hong Kong University of Science and Technology, Kowloon, Hong Kong)

  • Zuo-Jun Max Shen

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720)

Abstract

The benefits of inventory risk pooling are well known and documented. It has been proven in the literature that the expected costs of a centralized system are increasing in the degree of (positive) dependence of demand in an idealized newsvendor setting. Using the supermodular stochastic order to characterize dependence, we study a general two-tiered supply chain structure, in which both demand and supply yields are random, and prove that the expected costs are increasing in the degrees of positive dependence between demand and supply yield loss factors. Furthermore, using a distributionally robust optimization framework, we prove an analogous result for the case where demand and yield distributions are not precisely known.

Suggested Citation

  • Ho-Yin Mak & Zuo-Jun Max Shen, 2014. "Pooling and Dependence of Demand and Yield in Multiple-Location Inventory Systems," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 263-269, May.
    • Handle: RePEc:inm:ormsom:v:16:y:2014:i:2:p:263-269
    DOI: 10.1287/msom.2013.0469
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    References listed on IDEAS

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    Cited by:

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    3. Ying Rong & Ying‐Ju Chen & Zuo‐Jun Max Shen, 2015. "The impact of demand uncertainty on product line design under endogenous substitution," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(2), pages 143-157, March.
    4. Xiao, Li & Wang, Ce, 2023. "Multi-location newsvendor problem with random yield: Centralization versus decentralization," Omega, Elsevier, vol. 116(C).
    5. Güllü, Refik & Erkip, Nesim, 2024. "Risk pooling under demand and price uncertainty," European Journal of Operational Research, Elsevier, vol. 315(1), pages 120-129.
    6. Zheng Cui & Jianpeng Ding & Daniel Zhuoyu Long & Lianmin Zhang, 2023. "Target‐based resource pooling problem," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1187-1204, April.
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    8. Ming Zhao & Nickolas Freeman & Kai Pan, 2023. "Robust Sourcing Under Multilevel Supply Risks: Analysis of Random Yield and Capacity," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 178-195, January.
    9. Kostas Bimpikis & Mihalis G. Markakis, 2016. "Inventory Pooling Under Heavy-Tailed Demand," Management Science, INFORMS, vol. 62(6), pages 1800-1813, June.
    10. Silbermayr, Lena & Jammernegg, Werner & Kischka, Peter, 2017. "Inventory pooling with environmental constraints using copulas," European Journal of Operational Research, Elsevier, vol. 263(2), pages 479-492.
    11. Yuanguang Zhong & Zhichao Zheng & Mabel C. Chou & Chung-Piaw Teo, 2018. "Resource Pooling and Allocation Policies to Deliver Differentiated Service," Management Science, INFORMS, vol. 64(4), pages 1555-1573, April.
    12. Zhen Sun & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2019. "Data-Driven Decisions for Problems with an Unspecified Objective Function," INFORMS Journal on Computing, INFORMS, vol. 31(1), pages 2-20, February.
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    14. Lei Lei & Jun Ru & Ruixia Shi & Jun Zhang, 2022. "A Two‐Product Newsvendor Problem with Partial Demand Substitution," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1157-1173, March.

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