Bayesian Belief Network Approach for Supply Risk Modelling

Abstract

Today’s global and complex world increases the vulnerability to risks exponentially, and organizations are compelled to develop effective risk management strategies for mitigation. The prime focus of the research is to design a supply risk model using Bayesian belief network bearing in mind the tie-in of risk factors (i.e., objective and subjective) critical to a supply chain network. The proposed model can be re-engineered as per new information available at disclosure, so risk analysis will be current and relevant along the timeline as the situation is strained. The top three factors which influenced profitability were transportation risk and price risks. Netica is the platform used for designing and running simultaneous simulations on the Bayesian network. The proposed methodology is demonstrated through a case study conducted in an Indian manufacturing supply chain taking inputs from supply chain/risk management experts.