Bayesian networks for supply chain risk, resilience and ripple effect analysis: A
literature review
#MMPMID32834558
Hosseini S
; Ivanov D
Expert Syst Appl
2020[Dec]; 161
(?): 113649
PMID32834558
show ga
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 peer-reviewed 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.
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