Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89682
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dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorDong, Len_US
dc.creatorGeng, Xen_US
dc.creatorXiao, Gen_US
dc.creatorYang, Nen_US
dc.date.accessioned2021-04-28T02:29:13Z-
dc.date.available2021-04-28T02:29:13Z-
dc.identifier.issn1523-4614en_US
dc.identifier.urihttp://hdl.handle.net/10397/89682-
dc.language.isoenen_US
dc.publisherInstitute for Operations Research and the Management Sciencesen_US
dc.rights© 2021 INFORMSen_US
dc.rightsThis is the accepted manuscript of the following article: Lingxiu Dong, Xin Geng, Guang Xiao, Nan Yang (2021) Procurement Strategies with Unreliable Suppliers Under Correlated Random Yields. Manufacturing & Service Operations Management 24(1):179-195, which has been published in final form at https://doi.org/10.1287/msom.2020.0918.en_US
dc.subjectSupply diversificationen_US
dc.subjectCorrelated random yieldsen_US
dc.subjectNewsvendor modelen_US
dc.subjectPricingen_US
dc.subjectMultivariate dependence orderen_US
dc.titleProcurement strategies with unreliable suppliers under correlated random yieldsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage179en_US
dc.identifier.epage195en_US
dc.identifier.volume24en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1287/msom.2020.0918en_US
dcterms.abstractProblem definition: This paper studies the sourcing of a monopoly firm that procures from multiple unreliable suppliers to meet its deterministic/price-dependent demand. The suppliers’ production processes are unreliable and are modeled by correlated proportional random yields. Academic/practical relevance: As a proactive risk-mitigation tool, supply diversification has been widely studied in the literature, with the primary focus on independent supply risks. However, supply risks in practice may be correlated in nature for various reasons. By accounting for yield correlation among suppliers’ production processes, our work aims to help firms better manage their supply base and fully exploit the benefit of risk pooling through diversification. Methodology: Stochastic optimization serves as our main tool for analysis. Results: We formulate the firm’s problem in a general n-supplier setting and prove its structural properties. For a two-supplier case, we fully characterize the firm’s optimal sourcing decision and provide a unified measurement to quantify how yield correlation and characteristics jointly affect the supply base selection. Specifically, we show that when the two suppliers are highly positively correlated the firm may sole-source from the supplier with higher effective procurement cost (the procurement cost per expected delivered unit) and also higher reliability. In addition, as the production yields become more positively correlated, supply diversification becomes less likely, and the firm’s profit decreases. Moreover, assuming multivariate normally distributed yields, we generalize those results and relevant insights to the multiplesupplier case. We uncover the critical role played by yield correlation and illustrate the insufficiency of using effective procurement cost alone to qualify a supplier. Finally, we incorporate demand uncertainty to confirm the robustness of our findings. Managerial implications: Our results urge caution in selecting the optimal supply base when the yield risks are correlated. Particularly, yield correlation, effective procurement costs, and supplier reliability should be jointly taken into account; otherwise, ignoring any one of these factors may lead to suboptimal outcomes.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationManufacturing and service operations management, Jan.-Feb. 2022, v. 24, no. 1, 179-195en_US
dcterms.isPartOfManufacturing and service operations managementen_US
dcterms.issued2022-01-
dc.identifier.eissn1526-5498en_US
dc.description.validate202104 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0797-n02-
dc.identifier.SubFormID1664-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextGeneral Research Fund Grant PolyU 15507218en_US
dc.description.pubStatusPublisheden_US
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