Resilience analysis based on multi-layer network community detection of supply chain network

As the economic environment becomes increasingly complex, enhancing supply chain resilience is crucial for the operations and long-term development of enterprises. Real-world supply chains, encompassing components such as goods, warehouses, and plants, often contain complex network structures, making resilience analysis a challenging task. This paper addresses this challenge from a network analysis perspective. We project the complex supply chain network into single-mode, multi-layer networks focusing on plants and warehouses. Utilizing a multi-layer community detection method, we identify local clusters within these networks. By uncovering closely connected clusters, we reveal the flexibility and redundancy in production capabilities among different plants and warehouses. An empirical study using real-world data demonstrates that multi-layer network clustering effectively uncovers indirect capacity linkages between plants and warehouses. The findings from this community detection are beneficial for strategic capacity management, aiding enterprises in managing supply shortages or sudden demand spikes.