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On optimal emergency orders with updated demand forecast and limited supply

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  • Meimei Zheng
  • Yan Shu
  • Kan Wu

Abstract

This study analyses a two-stage newsvendor system with a regular and an emergency order. The emergency order can be placed at a later time based on a more accurate demand forecast. However, the unit cost for the emergency order is higher, and the quantity is limited. To maximise the expected profit, a retailer should determine both regular and emergency order quantities by considering the demand forecast updating, ordering cost and quantity constraint. Using dynamic programming, optimal ordering quantities are derived, and properties of the optimal solutions are obtained. Numerical experiments are carried out to illustrate the effect of the emergency order on the ordering decisions and expected profit. Some managerial insights are gained from the numerical results.

Suggested Citation

  • Meimei Zheng & Yan Shu & Kan Wu, 2015. "On optimal emergency orders with updated demand forecast and limited supply," International Journal of Production Research, Taylor & Francis Journals, vol. 53(12), pages 3692-3719, June.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:12:p:3692-3719
    DOI: 10.1080/00207543.2014.987882
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    Citations

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    Cited by:

    1. Liu, Weihua & Liu, Xiaoyan & Li, Xiang, 2015. "The two-stage batch ordering strategy of logistics service capacity with demand update," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 65-89.
    2. 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.
    3. Mohammed Hichame Benbitour & Evren Sahin & Yves Dallery, 2019. "The use of rush deliveries in periodic review assemble-to-order systems," Post-Print hal-01997380, HAL.
    4. Shao, Jianfang & Liang, Changyong & Liu, Yujia & Xu, Jian & Zhao, Shuping, 2021. "Relief demand forecasting based on intuitionistic fuzzy case-based reasoning," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    5. Xu, Qingyun & He, Yi & Shao, Zhen, 2023. "Retailer's ordering decisions with consumer panic buying under unexpected events," International Journal of Production Economics, Elsevier, vol. 266(C).
    6. Patra, T. Devi Prasad & Jha, J.K., 2021. "A two-period newsvendor model for prepositioning with a post-disaster replenishment using Bayesian demand update," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    7. Khouja, Moutaz & Christou, Eliana & Stylianou, Antonis, 2020. "A heuristic approach to in-season capacity allocation in a multi-product newsvendor model," Omega, Elsevier, vol. 95(C).
    8. Weihua Liu & Donglei Zhu & Yijia Wang, 2017. "Capacity Procurement in Logistics Service Supply Chain with Demand Updating and Rational Expectation Behavior," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(06), pages 1-48, December.
    9. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    10. Weihua Liu & Shuqing Wang & DongLei Zhu & Di Wang & Xinran Shen, 2018. "Order allocation of logistics service supply chain with fairness concern and demand updating: model analysis and empirical examination," Annals of Operations Research, Springer, vol. 268(1), pages 177-213, September.
    11. Bellenbaum, Julia & Höckner, Jonas & Weber, Christoph, 2022. "Designing flexibility procurement markets for congestion management – investigating two-stage procurement auctions," Energy Economics, Elsevier, vol. 106(C).

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