Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach

Abstract

The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in a single-echelon-single-event setting is desirable and indeed critical for some firms, modelling 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 suppliers. The proposed DTMC model is then equalised with a DBN model in order to simulate the propagation behaviour of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level. This ripple effect metric is applied to two case studies and analysed. The findings suggest that our model can be of value in uncovering latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritising contingency and recovery policies.

Keywords:

• supply chain dynamics
• supply chain resilience
• supply chain risk management
• Bayesian methods
• Markov modelling
• ripple effect

Acknowledgements

We are grateful to two anonymous reviewers for their invaluable improvement suggestions that greatly helped us in improving the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Dmitry Ivanov http://orcid.org/0000-0003-4932-9627

Alexandre Dolgui http://orcid.org/0000-0003-0527-4716

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.