Evaluation of control strategies for managing supply chain risks using Bayesian Belief Networks

Abstract

Supply chains have become complex and vulnerable and therefore, researchers are developing effective techniques in order to capture the complex structure of the supply network and interdependency between supply chain risks. Researchers have recently started using Bayesian Belief Networks for modelling supply chain risks. However, these models are still focused on limited domains of supply chain risk management like supplier selection, supplier performance evaluation and ranking. We have developed a comprehensive risk management process using Bayesian networks that captures all three stages of risk management including risk identification, risk assessment and risk evaluation. Our proposed new risk measures and evaluation scheme of different combinations of control strategies are considered as an important contribution to the literature. We have modelled supply network as a Bayesian Belief Network incorporating the supply network configuration, probabilistic interdependency between risks, resulting losses, risk mitigation control strategies and associated costs. An illustrative example is presented and three different models are solved corresponding to different risk attitudes of the decision maker. Based on our results, it is not always viable to implement control strategy at the most important risk factor because of the consideration of mitigation cost, relative loss and probabilistic interdependency between connected risk factors.