Coordinating contingent assistance of lateral suppliers under disruption

IF 3.6 2区 管理学 Q2 BUSINESS
Guanmei Liu, Rui-Sen Jiang, X. Shao
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引用次数: 1

Abstract

This paper focuses on coordinating contingent assistance between two lateral suppliers controlled by a central firm, when one supplier is exposed to supply disruption. By comparing two scenarios where the central firm can/cannot coordinate contingent assistance, we find the coordination of contingent assistance is more efficient, thus the central firm should do that. In the scenario without coordination, if the holding cost of the disrupted supplier is low, while the opportunity cost of the reliable supplier is high relatively, allowing the reliable supplier to hold decision power of assistance price can generate more assistance quantity for the disrupted supplier and bring more profits for the central firm. However, if the holding cost is high, and the opportunity cost is low relatively, the disrupted supplier can receive more assistance quantity, and the central firm can get more profits, by letting the disrupted supplier have the power to decide assistance price.
协调中断的横向供应商的应急援助
本文的重点是当一个供应商面临供应中断时,协调由中央公司控制的两个横向供应商之间的应急援助。通过比较中央企业可以/不能协调应急援助的两种情况,我们发现应急援助的协调更有效,因此中央企业应该这样做。在没有协调的情况下,如果被干扰供应商的持有成本较低,而可靠供应商的机会成本相对较高,允许可靠供应商持有援助价格的决策权,可以为被干扰供应商产生更多的援助数量,并为中心企业带来更多的利润。然而,如果持有成本较高,而机会成本相对较低,则通过让被干扰的供应商有权决定援助价格,被干扰供应商可以获得更多的援助数量,中心企业可以获得更多利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Management Analytics
Journal of Management Analytics SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
13.30
自引率
3.40%
发文量
14
期刊介绍: The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.
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