供应链中断持续时间信息的价值

Mili Mehrotra, W. Schmidt
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引用次数: 11

摘要

遇到供应链中断的公司经常面临一个艰难的决定——要么接受他们所掌握的关于中断持续时间的信息,要么投资收集更好的信息。这种选择并不明确,因为可能无法获得更好的信息,即使可以获得,也可能无法改善业务决策。鉴于这种困境,我们与一家财富500强制造公司的跨国部门合作,开发随机线性规划模型,量化中断持续时间信息的价值。我们的模型使我们能够检查可能与更好的信息价值相关的被破坏部分的特征。我们关注的是在混乱开始时就知道的特征,因为这些特征可以帮助公司决定是否投资收集更好的信息。利用我们的研究合作伙伴的供应链和生产数据,我们发现信息的价值可能有很大差异——从不足中断成本的1%到超过99%,强调了识别对信息质量敏感的中断的价值。为了解决这个问题,我们使用公司的数据来识别影响中断持续时间信息价值的几个与部件相关的特征。这些发现可以帮助管理者识别自己供应链中对中断的影响对不同持续时间信息敏感的部分,并允许他们在中断发生时做出是否收集更好信息的明智决定。
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The Value of Supply Chain Disruption Duration Information
Companies that experience a disruption in their supply chain often face a difficult decision -- either accept the information that they have regarding the duration of the disruption, or invest in collecting better information. This choice is not clear since better information may not be attainable, and if it is attainable, it may not improve operational decision-making. In light of this dilemma, we collaborate with a multinational division of a Fortune 500 manufacturing firm to develop stochastic linear programming models that quantify the value of disruption duration information. Our models allow us to examine characteristics of the disrupted part that may be associated with the value of better information. We focus on characteristics that are knowable at the outset of the disruption, as those can help the firm decide whether to invest in collecting better information. Using our research partner's supply chain and production data, we find that the value of information can vary materially - from less than 1% to over 99% of the cost of the disruption, underscoring the value of identifying disruptions that are sensitive to information quality. To address this, we use the company's data to identify several part-related characteristics that influence the value of disruption duration information. These findings can help managers to identify parts in their own supply chains whose impact in a disruption is sensitive to different levels of duration information, and allow them to make informed decisions on whether or not to gather better information when a disruption strikes.
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