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Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation

Author

Listed:
  • Abroon Qazi

    (American University of Sharjah)

  • Mecit Can Emre Simsekler

    (Khalifa University of Science and Technology)

  • Steven Formaneck

    (Canadian University Dubai)

Abstract

Several techniques have been proposed in supply chain risk management to capture causality among risks in a network setting and prioritize risks regarding their network-wide propagation impact. However, these techniques might be unable to capture the Risk Network Value at Risk (RNVaR), the maximum risk exposure expected at a given confidence level for a given timeframe, associated with individual supply chain performance measures within a network setting. With an exclusive focus on using point estimates in existing techniques, there is a risk of overlooking tail distributions and ignoring critical risks. In this paper, we aim to address this research gap by introducing new risk metrics and a new process theoretically grounded in Bayesian Belief Network and Monte Carlo Simulation frameworks. Integrating these two techniques helps establish the RNVaR that is associated with different performance measures and the relative importance of individual risks for resource allocation. We demonstrate the application of the proposed process through a real case study in the telecommunications industry and compare the results of this study with existing approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:322:y:2023:i:1:d:10.1007_s10479-022-04598-3
    DOI: 10.1007/s10479-022-04598-3
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    as
    1. Bimal Nepal & Om Prakash Yadav, 2015. "Bayesian belief network-based framework for sourcing risk analysis during supplier selection," International Journal of Production Research, Taylor & Francis Journals, vol. 53(20), pages 6114-6135, October.
    2. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    3. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    4. Nagurney, Anna & Matsypura, Dmytro, 2005. "Global supply chain network dynamics with multicriteria decision-making under risk and uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(6), pages 585-612, November.
    5. Trkman, Peter & McCormack, Kevin, 2009. "Supply chain risk in turbulent environments--A conceptual model for managing supply chain network risk," International Journal of Production Economics, Elsevier, vol. 119(2), pages 247-258, June.
    6. Chun-Hung Chiu & Tsan-Ming Choi, 2016. "Supply chain risk analysis with mean-variance models: a technical review," Annals of Operations Research, Springer, vol. 240(2), pages 489-507, May.
    7. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    8. Wilson, Martha C., 2007. "The impact of transportation disruptions on supply chain performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(4), pages 295-320, July.
    9. Aven, Terje, 2017. "Improving risk characterisations in practical situations by highlighting knowledge aspects, with applications to risk matrices," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 42-48.
    10. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    11. Ackermann, Fran & Howick, Susan & Quigley, John & Walls, Lesley & Houghton, Tom, 2014. "Systemic risk elicitation: Using causal maps to engage stakeholders and build a comprehensive view of risks," European Journal of Operational Research, Elsevier, vol. 238(1), pages 290-299.
    12. William Ho & Tian Zheng & Hakan Yildiz & Srinivas Talluri, 2015. "Supply chain risk management: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 5031-5069, August.
    13. Aven, Terje, 2016. "Supplementing quantitative risk assessments with a stage addressing the risk understanding of the decision maker," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 51-57.
    14. Wan, Chengpeng & Yan, Xinping & Zhang, Di & Qu, Zhuohua & Yang, Zaili, 2019. "An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 222-240.
    15. Berger, Paul D. & Gerstenfeld, Arthur & Zeng, Amy Z., 2004. "How many suppliers are best? A decision-analysis approach," Omega, Elsevier, vol. 32(1), pages 9-15, February.
    16. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    17. Guizhen Zhang & Vinh V. Thai & Adrian Wing‐Keung Law & Kum Fai Yuen & Hui Shan Loh & Qingji Zhou, 2020. "Quantitative Risk Assessment of Seafarers’ Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 8-23, January.
    18. Abdurrezzak Sener & Mehmet Barut & Ali Dag & Mehmet Bayram Yildirim, 2021. "Impact of commitment, information sharing, and information usage on supplier performance: a Bayesian belief network approach," Annals of Operations Research, Springer, vol. 303(1), pages 125-158, August.
    19. Ruiz-Benítez, Rocío & López, Cristina & Real, Juan C., 2018. "The lean and resilient management of the supply chain and its impact on performance," International Journal of Production Economics, Elsevier, vol. 203(C), pages 190-202.
    20. Norrington, Lisa & Quigley, John & Russell, Ashley & Van der Meer, Robert, 2008. "Modelling the reliability of search and rescue operations with Bayesian Belief Networks," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 940-949.
    21. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    22. Seyedmohsen Hosseini & Dmitry Ivanov & Alexandre Dolgui, 2020. "Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3284-3303, June.
    23. Boutselis, Petros & McNaught, Ken, 2019. "Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context," International Journal of Production Economics, Elsevier, vol. 209(C), pages 325-333.
    24. Qazi, Abroon & Quigley, John & Dickson, Alex & Ekici, Şule Önsel, 2017. "Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies," European Journal of Operational Research, Elsevier, vol. 259(1), pages 189-204.
    25. Aqlan, Faisal & Lam, Sarah S., 2015. "A fuzzy-based integrated framework for supply chain risk assessment," International Journal of Production Economics, Elsevier, vol. 161(C), pages 54-63.
    26. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    27. Dong, Qingxing & Cooper, Orrin, 2016. "An orders-of-magnitude AHP supply chain risk assessment framework," International Journal of Production Economics, Elsevier, vol. 182(C), pages 144-156.
    28. Tse, Ying Kei & Zhang, Minhao & Tan, Kim Hua & Pawar, Kulwant & Fernandes, Kiran, 2019. "Managing quality risk in supply chain to drive firm's performance: The roles of control mechanisms," Journal of Business Research, Elsevier, vol. 97(C), pages 291-303.
    29. Hosseini, Seyedmohsen & Morshedlou, Nazanin & Ivanov, Dmitry & Sarder, M.D. & Barker, Kash & Khaled, Abdullah Al, 2019. "Resilient supplier selection and optimal order allocation under disruption risks," International Journal of Production Economics, Elsevier, vol. 213(C), pages 124-137.
    30. Lianfa Li & Jinfeng Wang & Hareton Leung & Sisi Zhao, 2012. "A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk," Risk Analysis, John Wiley & Sons, vol. 32(6), pages 1072-1092, June.
    31. Jiang, Meizhi & Lu, Jing, 2020. "The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).
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