United We Stand or Divided We Stand? Strategic Supplier Alliances Under Order Default Risk

联盟 业务 订单(交换) 利润(经济学) 产业组织 背景(考古学) 下游(制造业) 上游(联网) 微观经济学 风险中性 供应链 竞赛(生物学) 经济 营销 财务 计算机科学 古生物学 计算机网络 生态学 政治学 法学 生物
作者
Xiao Huang,Tamer Boyacı,Mehmet Gümüş,Saibal Ray,Dan Zhang
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
卷期号:62 (5): 1297-1315 被引量:49
标识
DOI:10.1287/mnsc.2015.2175
摘要

We study the alliance formation strategy among suppliers in a framework with one downstream firm and n upstream suppliers. Each supplier faces an exogenous random shock that may result in an order default. Each of them also has access to a recourse fund that can mitigate this risk. The suppliers can share the fund resources within an alliance, but they need to equitably allocate the profits of the alliance among the partners. In this context, suppliers need to decide whether to join larger alliances that have better chances of order fulfillment or smaller ones that may grant them higher profit allocations. We first analytically characterize the exact coalition-proof Nash-stable coalition structures that would arise for symmetric complementary or substitutable suppliers. Our analysis reveals that it is the appeal of default risk mitigation, rather than competition reduction, that motivates cooperation. In general, a riskier and/or less fragmented supply base favors larger alliances, whereas substitutable suppliers and customer demands with lower pass-through rates result in smaller ones. We then characterize the stable coalition structures for an asymmetric supplier base. We establish that grand coalition is more stable when the supplier base is more homogeneous in terms of their risk levels, rather than divided among a few highly risky suppliers and other low-risk ones. Going one step further, our investigation of endogenous recourse fund levels for the suppliers demonstrates how financing costs affect suppliers’ investments in risk-reducing resources and, consequently, their coalition formation strategy. Last, we discuss model generalizations and show that, in general, our insights are quite robust. This paper was accepted by Serguei Netessine, operations management.
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