| dc.description.abstract | Our project sponsor, a major third-party logistics provider in Japan, experienced a severe disruption that destroyed one of their primary distribution centers for a specific industry. This disruption led to increased lead times, degraded service levels, higher logistics costs, and the loss of a client. Consequently, our research focused on supply chain disruptions and resiliency. We aimed to answer three research questions: (1) what was the loss caused by the disruption? (2) how should the network be rebuilt to recover from the disruption? (3) how can resiliency be added to mitigate the risk of future disruptions? We addressed these questions by collecting real- world data, including data before, during, and after the disruption. We then developed mixed- integer linear programming models of the pre-disruption network and networks optimized with additional candidate distribution centers. Then a scenario-planning approach was employed to evaluate the costs and resiliency of these models. Our results revealed the loss caused by the disruption (7.4% cost increase), the estimated improvement of the company's disruption recovery plan (3.5% cost reduction), and the potential to achieve a more resilient network without additional costs. The results can be used not only to recover from the disruption but also to enhance the efficiency and resiliency of their logistics network. Furthermore, our research highlights the potential utilization of the developed network model for mitigating future risks and enabling contingency planning in the event of network disruptions. | en_US |
