Ripple Effect Modeling of Supplier Disruption
Presented at the 7th Annual BayesiaLab Conference at the North Carolina Biotechnology Center.
Abstract
The ripple effect can occur when a supplier base disruption cannot be localized and consequently downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC design and assessment of their vulnerability to disruption in a single-echelon-single-event setting is desirable and indeed critical for some firms, modeling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, the ripple effect assessment in multi-stage SCs is particularly challenged by the need to consider both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on the integration of a Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) to quantify the ripple effect. We use the DTMC to model the recovery and vulnerability of the supplier.