When a supplier experiences a disruption, it learns how to better prevent and recover from future disruptions. As suppliers learn to become more resilient, the overall supply network also learns to become more resilient. This research draws on the organizational learning literature to introduce the concept of supply network resilience learning, which we define as the improvement of supply network resilience when suppliers learn from their own disruptions. The analysis integrates agent-based modeling, experimental design, data analytics, and analytical modeling to investigate how supplier learning improves supply network learning. We examine how two types of supplier learning, namely, learning-to-prevent and learning-to-recover, affect supply network learning. The results show that suppliers' learning-to-prevent results in a disruption-free supply network when time approaches infinity. However, the results differ across a more realistic finite time horizon. In this setting, learning-to-recover improves network learning when suppliers face a lower chance of disruption. The analysis also shows that centrally located suppliers enhance network learning, except when the risk of a disruption is high and the chance of diffusing a disruption to another supplier is high. In this setting, noncentral suppliers become more critical to supply network learning. This research provides a framework that will help practitioners understand the contingencies that influence the effect of supplier learning on the overall supply network resilience learning.

中文翻译：

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供应网络弹性学习：探索性数据分析研究

当供应商遇到中断时，它会学习如何更好地预防和从未来的中断中恢复。随着供应商学会变得更有弹性，整个供应网络也学会变得更有弹性。本研究利用组织学习文献引入供应网络弹性学习的概念，我们将其定义为供应商从自身中断中学习时供应网络弹性的提高。该分析集成了基于代理的建模、实验设计、数据分析和分析建模，以研究供应商学习如何改进供应网络学习。我们研究了两种类型的供应商学习方式，即学习预防和学习恢复，影响供应网络学习。结果表明，当时间接近无限时，供应商的学习预防导致供应网络无中断。然而，在更现实的有限时间范围内，结果会有所不同。在这种情况下，学习恢复当供应商面临较低的中断机会时，可以改善网络学习。分析还表明，位于中心的供应商增强了网络学习，除非中断的风险很高并且将中断传播给另一个供应商的机会很高。在这种情况下，非中心供应商对供应网络学习变得更加关键。这项研究提供了一个框架，可以帮助从业者了解影响供应商学习对整体供应网络弹性学习影响的偶然因素。