Measuring Circular Supply Chain Risk: A Bayesian Network Methodology

Abstract

The world is facing economic, as well as social, crisis due to the COVID-19 pandemic. Implementing sustainable practices is one of the possible ways to address these issues. Adopting circular oriented techniques throughout the supply chain not only guarantees economic profitability, but also provides an edge to the organization in the market of fierce global competition. The concept of implementing circularity in the supply chain is novel and dynamic in nature, and it involves certain risk. In this study, a Bayesian Network methodology is adopted to analyze how the risk propagation takes place in a circular supply chain network of an automobile organization. The circular supply chain network consists of a group of manufacturers, retailers and recyclers, located in the Delhi–NCR region. Economic, environmental, social, technological, waste management, agile vulnerability, and risk of cannibalization are the major risk categories that were identified through an extensive literature review. Further, the impact of risk on the performance of the circular supply chain is analyzed by considering performance parameters such as lost sales, impact on supply chain revenue, and inventory holding cost. Risk exposure index is incorporated into the study to analyze the vulnerability of each node. The findings of the study reveal that the reverse side of the circular supply chain can be a source of risk propagation during the implementation of the circularity concept. This work is carried out under a single industry domain. In the future, risk propagation analysis can be examined in the supply chain of other sectors. The findings of the study can assist the supply chain managers and the risk experts to focus on the areas that are more vulnerable to risk.