IDEAS home Printed from https://ideas.repec.org/r/taf/tprsxx/v56y2018i17p5795-5819.html
   My bibliography  Save this item

Bayesian network modelling for supply chain risk propagation

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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.
  2. 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.
  3. 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).
  4. 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).
  5. 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).
  6. 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).
  7. 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.
  8. 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).
  9. 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.
  10. Lydia Novoszel & Tina Wakolbinger, 2022. "Meta-analysis of Supply Chain Disruption Research," SN Operations Research Forum, Springer, vol. 3(1), pages 1-25, March.
  11. 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.
  12. 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).
  13. 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).
  14. 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).
  15. 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).
  16. 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.
  17. 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.
  18. 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).
  19. 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.
  20. 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.
  21. 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.
  22. 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).
  23. 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.
  24. 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.
  25. 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).
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. Sardesai, Saskia & Klingebiel, Katja, 2023. "Maintaining viability by rapid supply chain adaptation using a process capability index," Omega, Elsevier, vol. 115(C).
  32. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.