Probabilistic Risk Assessment in Realistic Supply Chain Networks
- 井手 清貴（防衛大学校）
- 関連キーワード ：
Because of dramatic development of global supply chain and diversity of the products and consumers demands, supply chain networks (SCNs) have become more and more complex and difficult to find potential risks in SCNs. However, the resent experiences of the supply chain disruptions caused by the significant natural disasters highlight the importance of quantification and identification of the risks in SCNs. In this talk, we assess and analyze the vulnerability and importance of each entity in SCNs utilizing the probabilistic approaches. First, we utilized Bayesian network approach to measure the disruption probability of each entity in some real SCNs. Then, utilizing Susceptible-Infected-Removed (SIR) model, we developed measures to quantify the vulnerability and importance of each entity in SCNs, Vulnerability Index (VI) and Amplification Index (AI), which fit with the results of the Bayesian network approach. In addition, we derived an analytical framework to calculate the VI and AI without simulations. Furthermore, we developed a framework to compute VI and AI based on the weighted transition probability matrices which are obtained from end consumers’ demands. The results of this work will be useful to design the robust SCNs or develop the effective counter measure to address the supply chain disruptions.