# Research on Supply Chain Network Resilience: Considering Risk Propagation and Node Type
###### Abstract
Resilience metrics such as the number of healthy nodes [27], the size of the largest subgraph, and the average path length [28] have been used frequently. However, all of the aforementioned metrics share the common limitation of a consistent type of different nodes [29]. In a real-life SCN, if a certain type of node is lacking, this SCN will not function properly. The current maximum full-type subgraph metric is calculated by removing nodes at each time step, which can damage the network structure [30, 31]. The SCNR metrics proposed in this study differ in two distinct respects: First, we performed the analysis after the SCN risk propagation reached a steady state. The average (i.e., a novel SCNR metric considering node type) of the sizes of the maximum connected subgraphs over a period of time after reaching a steady state was used to measure the resilience of SCNs. Second, we performed the calculation without destroying the network structure. We computed the maximum full-type subgraph formed by using healthy nodes, where the SCN structure is always complete. The purpose of this study is to investigate the effects of different network characteristic parameters on SCNR when risk propagation occurs in SCNs, and to propose some guiding suggestions for supply chain management to improve SCNR. According to the experimental results of this study, supply chain managers can optimize the SCN structure to improve the SCNR. Understanding how the multiple network characteristics of the SCN affect its resilience can help society ensure the continuous supply of critical social materials and services in the face of emergencies to maintain the normal operation of society. In addition, optimizing the SCN can adjust the allocation of social resources to save social resources and promote the sustainable development of society. This research also has significance for practice. By understanding the influence factors of SCNR, it can help enterprises optimize their supply chain management to have a clearer understanding of their own supply chain status, improve their competitiveness, and help them to formulate more effective risk strategies to reduce the impacts of risks. The government can formulate more scientific and effective economic policies by studying the network characteristics that affect SCNR. Building a more resilient supply chain ensures that consumers receive a reliable supply of products and better service. The problem investigated in this study is how the network characteristics of SCNR, such as node types, scalar indices, and clustering coefficients, are affected when the risk propagation on an SCN reaches a steady state. The rest of the paper is organized as follows. Section 2 presents a literature review. Section 3 presents the details of the proposed SCN risk propagation model and resilience metrics. Section 4 presents the experimental setup and analysis of the results. Section 5 presents some broader practical insights based on the analysis. Section 6 validates the results by using a real-life SCN. Section 7 summarizes the contributions and shortcomings of this study and discusses some future research directions.