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Bayesian belief network-based framework for sourcing risk analysis during supplier selection

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  • Bimal Nepal
  • Om Prakash Yadav

Abstract

Increasing trend in global business integration and movement of material around the world has caused supply chain system susceptible to disruption involving higher risks. This paper presents a methodology for supplier selection in a global sourcing environment by considering multiple cost and risk factors. Failure modes and effects analysis technique from reliability engineering field and Bayesian belief networks are used to quantify the risk posed by each factor. The probability and the cost of each risk are then incorporated into a decision tree model to compute the total expected costs for each supply option. The supplier selection decision is made based on the total purchasing costs including both deterministic costs (such as product and transportation costs) and the risk-associated costs. The proposed approach is demonstrated using an example of a US-based Chemical distributor. This framework provides a visual tool for supply chain managers to see how cost and risks are distributed across the different alternatives. Lastly, managers can calculate expected value of perfect information to avoid a certain risk.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:20:p:6114-6135
    DOI: 10.1080/00207543.2015.1027011
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    Citations

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    Cited by:

    1. Nikelowski, Lukas & Voss, Rika, 2021. "Approach for application-specific selection of risk assessment methods," 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 853-877, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Surya Prakash & Sameer Kumar & Gunjan Soni & Vipul Jain & Ajay Pal Singh Rathore, 2020. "Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach," Annals of Operations Research, Springer, vol. 290(1), pages 837-864, July.
    3. Mohebalizadehgashti, Fatemeh & Zolfagharinia, Hossein & Amin, Saman Hassanzadeh, 2020. "Designing a green meat supply chain network: A multi-objective approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 312-327.
    4. Xiaoqing Li & Qingquan Jiang & Maxwell K. Hsu & Qinglan Chen, 2019. "Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network," Sustainability, MDPI, vol. 11(17), pages 1-12, August.
    5. Faghih-Roohi, Shahrzad & Akcay, Alp & Zhang, Yingqian & Shekarian, Ehsan & de Jong, Eelco, 2020. "A group risk assessment approach for the selection of pharmaceutical product shipping lanes," International Journal of Production Economics, Elsevier, vol. 229(C).
    6. Gülcan Petriçli & Gül Gökay Emel, 2016. "Determining Strategy Based Supplier Pre-Qualification Criteria With Fuzzy Relational Maps," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 11-40, September.
    7. 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.
    8. Radoslav Delina & Renata Olejarova & Petr Doucek, 2023. "Effect of a new potential supplier on business to business negotiations performance: evidence-based analysis," Electronic Commerce Research, Springer, vol. 23(3), pages 1941-1970, September.
    9. Zhou, Rui & Bhuiyan, Tanveer Hossain & Medal, Hugh R. & Sherwin, Michael D. & Yang, Dong, 2022. "A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk," Omega, Elsevier, vol. 107(C).
    10. Yash Daultani & Mohit Goswami & Omkarprasad S. Vaidya & Sushil Kumar, 2019. "Inclusive risk modeling for manufacturing firms: a Bayesian network approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2789-2803, December.

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