The topological structure of global supply chain networks (SCNs) has become increasingly complex. In recent years, major disruption risk events have emerged, disrupting complex SCNs. Since a global SCN is the aggregation of several sub-SCNs in terms of industries and countries (regions) and is temporally changing in its topological characteristics, robustness toward risk should be analysed by the community in consideration of its temporality. In this study, we aim to generate temporal supply chain networks and track the evolution of their constituent communities over time, to evaluate the robustness of each SCN against error and attack risks, and to identify the topological features influencing the robustness of SCNs using real transaction data between firms. As a result, eight SCNs were detected based on industries and countries, and the size of these SCNs increased over time. The average shortest path length and degree distribution have similar impacts on each SCN, while the cluster structure diverges among SCNs. Regarding robustness against error and attack risk, unlike in existing studies, the SCNs are significantly partitioned from the largest connected component at the initial firm removal rate. Only for attack risk, percolation transition was found at approximately 10% removal of firms. Also, it is found that the significant topological features affecting robustness differ by type of risk.
Kawasaki et al. (Tue,) studied this question.