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Bayesian network modelling for supply chain risk propagation

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Cited by:

  1. Long Ren & Shaojie Cong & Xinlong Xue & Daqing Gong, 2024. "Credit rating prediction with supply chain information: a machine learning perspective," Annals of Operations Research, Springer, vol. 342(1), pages 657-686, November.
  2. Paul Souma Kanti & Riaz Sadia & Das Suchismita, 2022. "Artificial intelligence adoption in supply chain risk management: Scale development and validation," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 12(2), pages 15-32.
  3. Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
  4. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
  5. Sajid, Zaman, 2021. "A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  6. Sun, Xuting & Hu, Yue & Qin, Yichen & Zhang, Yuan, 2024. "Risk assessment of unmanned aerial vehicle accidents based on data-driven Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
  7. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
  8. Yang, Qing & Zou, Xingqi & Ye, Yunting & Yao, Tao, 2022. "Evaluating the criticality of the product development project portfolio network from the perspective of risk propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
  9. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
  10. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "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, Elsevier, vol. 232(C).
  11. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
  12. Xingqi Zou & Qing Yang & Qinru Wang & Bin Jiang, 2024. "Measuring the system resilience of project portfolio network considering risk propagation," Annals of Operations Research, Springer, vol. 340(1), pages 693-721, September.
  13. Lydia Novoszel & Tina Wakolbinger, 2022. "Meta-analysis of Supply Chain Disruption Research," SN Operations Research Forum, Springer, vol. 3(1), pages 1-25, March.
  14. Jiakuan Chen & Haoyu Wen, 2023. "The application of complex network theory for resilience improvement of knowledge-intensive supply chains," Operations Management Research, Springer, vol. 16(3), pages 1140-1161, September.
  15. Li, Zhuyue & Zhao, Peixin & Han, Xue, 2022. "Agri-food supply chain network disruption propagation and recovery based on cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
  16. Ghadge, Abhijeet & van der Werf, Sjoerd & Er Kara, Merve & Goswami, Mohit & Kumar, Pankaj & Bourlakis, Michael, 2020. "Modelling the impact of climate change risk on bioethanol supply chains," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
  17. Garvey, Myles D. & Carnovale, Steven, 2020. "The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation," International Journal of Production Economics, Elsevier, vol. 228(C).
  18. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
  19. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.
  20. Satyendra Kumar Sharma & Praveen Ranjan Srivastava & Ajay Kumar & Anil Jindal & Shivam Gupta, 2023. "Supply chain vulnerability assessment for manufacturing industry," Annals of Operations Research, Springer, vol. 326(2), pages 653-683, July.
  21. Kamble, Sachin S. & Gunasekaran, Angappa & Kumar, Vikas & Belhadi, Amine & Foropon, Cyril, 2021. "A machine learning based approach for predicting blockchain adoption in supply Chain," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
  22. Kraude, Richard & Narayanan, Sriram & Talluri, Srinivas, 2022. "Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1168-1182.
  23. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
  24. Ualison Rébula Oliveira & Camila Oliveira Santos & Gabriel Elias Lunz Chaves & Vicente Aprigliano Fernandes, 2022. "Analysis of the MORT method applicability for risk management in supply chains," Operations Management Research, Springer, vol. 15(3), pages 1361-1382, December.
  25. Belhadi, Amine & Kamble, Sachin & Jabbour, Charbel Jose Chiappetta & Gunasekaran, Angappa & Ndubisi, Nelson Oly & Venkatesh, Mani, 2021. "Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
  26. Gabrielle Gauthier Melançon & Philippe Grangier & Eric Prescott-Gagnon & Emmanuel Sabourin & Louis-Martin Rousseau, 2021. "A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains," Interfaces, INFORMS, vol. 51(3), pages 200-212, May.
  27. Yu Gong & Xiaojiang Xu & Changping Zhao & Tobias Schoenherr, 2024. "Multi-Tier Supply Chain Learning Networks: A Simulation Study Based on the Experience-Weighted Attraction (EWA) Model," Sustainability, MDPI, vol. 16(10), pages 1-25, May.
  28. Madhukar Chhimwal & Saurabh Agrawal & Girish Kumar, 2021. "Measuring Circular Supply Chain Risk: A Bayesian Network Methodology," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
  29. Liu, Yang & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao & Han, Bing, 2024. "A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  30. Abroon Qazi & Mecit Can Emre Simsekler & Steven Formaneck, 2023. "Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation," Annals of Operations Research, Springer, vol. 322(1), pages 241-272, March.
  31. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
  32. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
  33. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
  34. Brylowski, Martin & Schröder, Meike & Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Machine learning in supply chain management: A scoping review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 377-406, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  35. Sardesai, Saskia & Klingebiel, Katja, 2023. "Maintaining viability by rapid supply chain adaptation using a process capability index," Omega, Elsevier, vol. 115(C).
  36. Yijun Liu & Xiaokun Jin & Yunrui Zhang, 2024. "Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-21, December.
  37. Niels Bugert & Rainer Lasch, 2023. "Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks," Journal of Business Economics, Springer, vol. 93(5), pages 859-889, July.
  38. Yutong Liu & Jian Du & Taewon Kang & Mingu Kang, 2024. "Establishing supply chain transparency and its impact on supply chain risk management and resilience," Operations Management Research, Springer, vol. 17(3), pages 1157-1171, September.
  39. Liu, Ming & Lin, Tao & Chu, Feng & Ding, Yueyu & Zheng, Feifeng & Chu, Chengbin, 2023. "Bi-objective optimization for supply chain ripple effect management under disruption risks with supplier actions," International Journal of Production Economics, Elsevier, vol. 265(C).
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