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https://doi.org/10.58195/iaet.v2i1.135
Liu, X. . (2023). Viewpoints on Robust Supply Chain Network Risk Assessment Using Dynamic Bayesian Networks. Innovations in Applied Engineering and Technology, 2(1), 1–8. https://doi.org/10.58195/iaet.v2i1.135
Volume 2, Issue 1

Viewpoints on Robust Supply Chain Network Risk Assessment Using Dynamic Bayesian Networks

The text dissects the exploration of employing dynamic Bayesian networks (DBNs) for reliably assessing the risk posed to supply chain networks. A growing recognition of the complexity and intricacy of contemporary supply chain networks has sparked interest in novel methodologies such as DBNs that can model temporal and spatial dependencies effectively. DBNs emerge as a powerful analytical tool enabling probabilistic inference under prevailing uncertainties, capturing both the inherent and external risks. The paper demonstrates how these networks, bestowed with both temporal and causal mathematical frameworks, offer superior analytical traction over traditional methods in understanding the interconnected risk elements. We discuss their ability to appraise the ever-changing vulnerabilities in a supply chain setting, dynamically adapting to modifications brought by stakeholder actions or market changes. As supply chains burgeon in complexity, the role of DBNs in enabling a robust, comprehensive understanding of risk factors becomes increasingly paramount. Thus, the paper advocates for the DBNs' application as a transformative approach to risk assessment, providing significant foresight and adaptability in an unpredictable supply chain landscape.

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References

  1. Soberanis D, Elizabeth I. An Extended Bayesian Network Approach for Analyzing Supply Chain Disruptions. University of Iowa 2010. DOI: 1017077/etd.e4hwob69.
  2. Luo Z, Xu H, Chen F. Audio Sentiment Analysis by Heterogeneous Signal Features Learned from Utterance-Based Parallel Neural Network. AAAI-2019 Workshop on Affective Content Analysis, Honolulu, HI, USA, 27 January 2019. DOI: https://doi.org/10.29007/7mhj
  3. Chen F, Luo Z, Xu Y, Ke D. Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis. AAAI-2020 Workshop on Affective Content Analysis, New York, NY, USA, 7 February 2020.
  4. Luo Z, Zeng X, Bao Z, Xu M. Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography. 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019. DOI: https://doi.org/10.1109/IJCNN.2019.8851972
  5. Liu BC. Supply Chain Risk Evaluation Based on Bayesian Network. Advanced Materials Research 2013; 680: 550–553. DOI: https://doi.org/10.4028/www.scientific.net/AMR.680.550
  6. Qazi A, Quigley J, Dickson A. A Novel Framework for Quantification of Supply Chain Risks. 4th Student Conference on Operational Research, Nottingham, UK, 2–4 May 2014.
  7. Luo Z. Knowledge-Guided Aspect-Based Summarization. 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI), Shanghai, China, 23–25 June 2023. DOI: https://doi.org/10.1109/CCCAI59026.2023.00012
  8. Shafieezadeh A, Wang Z. A Parallel Learning Strategy for Adaptive Kriging-Based Reliability Analysis Methods. 13th International Conference on Applications of Statistics and Probability in Civil Engineering, Seoul, South Korea, 26–30 May 2019.
  9. Darestani YM, Wang Z, Shafieezadeh A. Wind Reliability of Transmission Line Models using Kriging-Based Methods. 13th International Conference on Applications of Statistics and Probability in Civil Engineering ICASP13, Seoul, South Korea, 26–30 May 2019.
  10. Song C, Wang Z, Shafieezadeh A, Xiao R. BUAK-AIS: Efficient Bayesian Updating With Active Learning Kriging-Based Adaptive Importance Sampling. Computer Methods in Applied Mechanics and Engineering 2022; 391: 114578. DOI: https://doi.org/10.1016/j.cma.2022.114578
  11. Qazi A, Quigley J, Dickson A, Gaudenzi B, Ekici SO. Evaluation of Control Strategies for Managing Supply Chain Risks Using Bayesian Belief Networks. 2015 International Conference on Industrial Engineering and Systems Management (IESM), Seville, Spain, 21–23 October 2015. DOI: https://doi.org/10.1109/IESM.2015.7380298
  12. Garvey MD, Carnovale S, Yeniyurt S. An Analytical Framework for Supply Network Risk Propagation: A Bayesian Network Approach. European Journal of Operational Research 2015; 243(2): 618–627. DOI: https://doi.org/10.1016/j.ejor.2014.10.034
  13. Yang J, Liu H. Research of Vulnerability for Fresh Agricultural-Food Supply Chain Based on Bayesian Network. Mathematical Problems in Engineering 2018; 2018(5): 1–17. DOI: https://doi.org/10.1155/2018/6874013
  14. Ojha R, Ghadge A, Tiwari MK, Bititci U. Bayesian Network Modelling for Supply Chain Risk Propagation. International Journal of Production Research 2018; 56(1). DOI: 10.1080/00207543.2018.1467059. DOI: https://doi.org/10.1080/00207543.2018.1467059
  15. Yang B, Xie L. An Early Warning System for "Direct Farm" Mode Based Agricultural Supply Chain. Proceedings of the 2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques (SMONT 2019), Shenzhen, China, 27 February 2019. DOI: https://doi.org/10.2991/smont-19.2019.25
  16. Kang L, Chu Y, Leng K, Van Nieuwenhuyse I. Construction of Fast Retrieval Model of E-commerce Supply Chain Information System Based on Bayesian Network. Information Systems and e-Business Management 2019; 18(2): 705–722. DOI: https://doi.org/10.1007/s10257-018-00392-6
  17. Qazi A, Dickson A, Quigley J, Gaudenzi B. Supply Chain Risk Network Management: A Bayesian Belief Network and Expected Utility Based Approach for Managing Supply Chain Risks. International Journal of Production Economics 2018; 196: 24–42. DOI: https://doi.org/10.1016/j.ijpe.2017.11.008
  18. Zhang C, Wang Z, Shafieezadeh A. Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging. IEEE Access 2020; 8: 51021–51034. DOI: https://doi.org/10.1109/ACCESS.2020.2980228
  19. Wang Z, Shafieezadeh A. REAK: Reliability Analysis Through Error Rate-Based Adaptive Kriging. Reliability Engineering & System Safety 2019; 182: 33–45. DOI: https://doi.org/10.1016/j.ress.2018.10.004
  20. Wang Z, Shafieezadeh A. ESC: An Efficient Error-Based Stopping Criterion for Kriging-Based Reliability Analysis Methods. Struct Multidisc Optim 2019; 59(5). DOI:10.1007/s00158-018-2150-9. DOI: https://doi.org/10.1007/s00158-018-2150-9

Supporting Agencies

  1. Funding: Not applicable.