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BayesiaLab Conference

Conference Presentation

Ripple Effect Modeling of Supplier Disruption: Integrated Markov Chain and Dynamic Bayesian Network Approach

Mohsen Hosseini, Assistant Professor, Industrial Engineering Technology
University of Southern Mississippi

Abstract

The ripple effect can occur when a supplier base disruption cannot be localised 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, 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 integration of 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 supplier.