Modelling risks in supply chains using Petri net approach

In supply chains, risk analysis involves the process of identifying threats and system vulnerabilities to determine consequences and estimate the expected loss. To analyse any risk environment, it is vital to know the paths of threat and the probability associated with each. Therefore, a complete structure and inference engine are required to determine the most probable path and the relative probabilities of occurrence for any chain of events. Petri Nets (PNs) are considered best to model the discrete event system. This paper introduces a novel approach that uses PNs to model the discrete event behaviour of supply chains and incorporates the max-min inferencing method used in fuzzy reasoning to incorporate the subjective probabilities of events associated with the supply chain process. The proposed approach generates its model based on matrices and performs inferencing automatically. The proposed model uses subjective probability thresholds based on military (MIL) standards and can derive risk analysis results under a changing environment.