At the recent BayesiaLab Conference, Dr. Mohsen Hosseini gave a talk on modeling ripple effects in supply chains with Dynamic Bayesian Networks. Here is the recording of his presentation.

 

Mohsen Hosseini, Ph.D., 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.

Bayesia_2019_conf_970x90_frame

Topics: Events, Conference, 2019 Conference