Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review
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Abstract
In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique
methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of disruptions. BNs have transitioned from an emerging topic to a growing research
area in supply chain (SC) resilience and risk analysis. As a result, there is an acute need to review existing
literature to ascertain recent developments and uncover future areas of research. Despite the increasing
number of publications on BNs in the domain of SC uncertainty, an extensive review on their application
to SC risk and resilience is lacking. To address this gap, we analyzed research articles published in peerreviewed academic journals from 2007 to 2019 using network analysis, visualization-based scientometric
analysis, and clustering analysis. Through this study, we contribute to literature by discussing the challenges of current research, and, more importantly, identifying and proposing future research directions.
The results of our survey show that further debate on the theory and application of BNs to SC resilience
and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed.
Link to resource
https://doi.org/10.1016/j.eswa.2020.113649Collections
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