An interactive decision support method for measuring risk in a complex supply chain under uncertainty

Abstract

Supply chains are becoming more vulnerable because of harsher and more frequent natural and manmade disasters. Supply chain disruptions now seem to occur more frequently and with more serious consequences. During and after supply chain disruptions, companies may lose revenue and incur high recovery costs. Therefore, if supply chain managers were able to better measure and manage supply chain vulnerability, they might be able to reduce the number of disruptions and their impacts. However, how to measure such risk is still an emerging topic for both research and practice. This paper presents a new interactive decision support method for measuring such risk using Value at Risk (VaR) and Conditional Value at Risk (CVaR). The proposed method, based on a disruption recovery model consisting of abrupt, linear and exponential modes, aims to help supply chain managers conduct "whatif" analyses, in order to tackle such vulnerability and other risk factors that would affect their business continuity. © 2013 IEEE.