Ripple Effect Modeling of Supplier Disruption

Ripple Effect Modeling of Supplier Disruption

Presented at the 7th Annual BayesiaLab Conference at the North Carolina Biotechnology Center.


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.

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