부산대학교 도서관

Keywords: network learning; network resilience; risk propagation; supply network; supplier management Abstract 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. Biographical information: Kedong Chen is an assistant professor in the Department of Information Technology & Decision Sciences, the Strome College of Business at Old Dominion University. He obtained his PhD at the Carlson School of Management, University of Minnesota. His research interests include supply chain network, operations strategy, supply chain risk management, empirical analysis, and data analytics in supply chain management. Yuhong Li is an assistant professor in the Department of Information Technology & Decision Sciences, the Strome College of Business at Old Dominion University. Her research interests include supply chain risk management, network analysis, supply chain risk propagation, and supply chain sustainability. Kevin Linderman is John J. Coyle Professor of Logistics and Supply Chain Management and the chair of Supply Chain & Information Systems Department at the Smeal College of Business at Penn State University. The Decision Science Journal recently ranked him as one of the most productive scholars in operations management. His research interest includes quality management, supply chain risk management, supply networks, and strategy. CAPTION(S): Data S1 Byline: Kedong Chen, Yuhong Li, Kevin Linderman