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dc.creatorHosseini, Seyedmohsen
dc.creatorIvanov, Dmitry
dc.date.accessioned2020-08-24T14:05:30Z
dc.date.available2020-08-24T14:05:30Z
dc.date.created2020
dc.identifier.issn0957-4174spa
dc.identifier.otherhttps://doi.org/10.1016/j.eswa.2020.113649spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/12130
dc.description.abstractIn 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.spa
dc.format.extent20 páginasspa
dc.format.mimetypeimage/jepgspa
dc.language.isoengspa
dc.publisherExpert Systems with Applicationsspa
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectSupply chain managementspa
dc.subjectSupply chain resiliencespa
dc.subjectBayesian networkspa
dc.subjectMachine learningspa
dc.subjectRipple effectspa
dc.titleBayesian networks for supply chain risk, resilience and ripple effect analysis: A literature reviewspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.localAcceso restringidospa
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2020.113649spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa


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