ArticleExpert systems with applications2020
Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review.
Article in Expert systems with applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 288 citations in OpenAlex.
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- Intelligent management and legal regulation of enterprise green supply chain by fuzzy comprehensive evaluation.Heliyon · 2024Article
- Assessing risk associated with recreational activities in coastal areas by using a bayesian network.Heliyon · 2023Article
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- Nexus between drivers of COVID-19 and country risks.Socio-economic planning sciences · 2023Article
- Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions.Expert systems with applications · 2023Article
- Just-in-time approach in healthcare inventory management: Does it really work?Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2022Review
- Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective.Annals of operations research · 2022Article
- Digital Innovation, Data Analytics, and Supply Chain Resiliency: A Bibliometric-based Systematic Literature Review.Annals of operations research · 2022Article
- Logistics and supply chain management of food industry during COVID-19: disruptions and a recovery plan.Environment systems & decisions · 2022Article
- Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context.Computers & industrial engineering · 2021Article
- Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic.Computers & industrial engineering · 2021Article
- A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields.Annals of operations research · 2021Article
- Sustainable supply chain management towards disruption and organizational ambidexterity: A data driven analysis.Sustainable production and consumption · 2021Review
- Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation.Annals of operations research · 2021Article
- OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications.International journal of production economics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.