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Coordinating a supply chain under uncertain demand and random yield in presence of supply disruption

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  • Bibhas Chandra Giri
  • Sudarshan Bardhan

Abstract

A two-echelon supply chain involving one manufacturer and one retailer for a single product is considered in this paper. The end customers’ demand is assumed to be random. The production of the manufacturer is subject to random yield, and there is a possibility of supply disruption in which case no item from her can reach the retailer. The retailer has a backup supplier who is costlier but perfectly reliable, and is having a limit up to which he may deliver. In addition to placing an order to the manufacturer, the retailer is allowed to reserve a quantity from the backup supplier in the ordering period; he may buy up to the reserved quantity after realising actual market demand in the trading period. Aiming at studying the effects of the various uncertainties involved in the chain on the optimal decisions, we develop and analyse centralised and decentralised models. We also propose a contract mechanism to coordinate the chain and find threshold conditions for which the coordinated model would collapse. Numerical examples are provided to illustrate the developed model.

Suggested Citation

  • Bibhas Chandra Giri & Sudarshan Bardhan, 2015. "Coordinating a supply chain under uncertain demand and random yield in presence of supply disruption," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 5070-5084, August.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:16:p:5070-5084
    DOI: 10.1080/00207543.2015.1030469
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    Citations

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    Cited by:

    1. Ghavamifar, Ali & Makui, Ahmad & Taleizadeh, Ata Allah, 2018. "Designing a resilient competitive supply chain network under disruption risks: A real-world application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 87-109.
    2. Hsieh, Chung-Chi & Lai, Hsing-Hua, 2017. "Capacity allocation with differentiated product demands under dual sourcing," International Journal of Production Economics, Elsevier, vol. 193(C), pages 757-769.
    3. Zare, Marjan & Esmaeili, Maryam & He, Yuanjie, 2019. "Implications of risk-sharing strategies on supply chains with multiple retailers and under random yield," International Journal of Production Economics, Elsevier, vol. 216(C), pages 413-424.
    4. Mohammadi, Mir Ahmad & Sayadi, Ahmad Reza & Khoshfarman, Mahsa & Husseinzadeh Kashan, Ali, 2022. "A systems dynamics simulation model of a steel supply chain-case study," Resources Policy, Elsevier, vol. 77(C).
    5. Shao, Xiao-Feng, 2018. "Production disruption, compensation, and transshipment policies," Omega, Elsevier, vol. 74(C), pages 37-49.
    6. Giri, B.C. & Bardhan, Sudarshan, 2017. "Sub-supply chain coordination in a three-layer chain under demand uncertainty and random yield in production," International Journal of Production Economics, Elsevier, vol. 191(C), pages 66-73.
    7. Papachristos, Ioannis & Pandelis, Dimitrios G., 2022. "Newsvendor models with random supply capacity and backup sourcing," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1231-1243.
    8. Baruah, Pundarikaksha & Chinnam, Ratna Babu & Korostelev, Alexander & Dalkiran, Evrim, 2016. "Optimal soft-order revisions under demand and supply uncertainty and upstream information," International Journal of Production Economics, Elsevier, vol. 182(C), pages 14-25.
    9. Zhao, Yujie & Zhou, Hong & Leus, Roel, 2022. "Recovery from demand disruption: Two-stage financing strategy for a capital-constrained supply chain under uncertainty," European Journal of Operational Research, Elsevier, vol. 303(2), pages 699-718.
    10. Hezarkhani, Behzad & Demirel, Guven & Bouchery, Yann & Dora, Manoj, 2023. "Can “ugly veg” supply chains reduce food loss?," European Journal of Operational Research, Elsevier, vol. 309(1), pages 117-132.

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