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A supply chain network equilibrium model for operational and opportunism risk mitigation

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  • Yash Daultani
  • Sushil Kumar
  • Omkarprasad S. Vaidya
  • Manoj K. Tiwari

Abstract

Risk management holds a crucial role in ensuring efficiency, predictability, and coherency in supply chain operations of an enterprise. Risks are associated with every member of a supply chain network. Thus, an end-to-end risk management approach is essential to fortify the entire supply chain network. In this paper, we consider a supply chain network consisting of suppliers, manufacturers, distributors and retailers, as the representative stakeholders. In particular, we take supply chain operational, and opportunism risks into account, and investigate the roles of flexibility, and social relationship, respectively, as a mitigation approach. We develop a multi-period network equilibrium model by considering the stakeholders’ objectives of maximising profit and minimising risk. Further, the finite-dimensional variational inequality formulations are derived for the underlying network optimisation problem. An algorithm, with nice features for computations, is then applied to three simulated examples in order to illustrate the model and computational procedure as well as the types of interventions that can help the strategic decision-makers to explore quantitatively the associated profits and incurred risks in an entire supply chain network.

Suggested Citation

  • Yash Daultani & Sushil Kumar & Omkarprasad S. Vaidya & Manoj K. Tiwari, 2015. "A supply chain network equilibrium model for operational and opportunism risk mitigation," International Journal of Production Research, Taylor & Francis Journals, vol. 53(18), pages 5685-5715, September.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:18:p:5685-5715
    DOI: 10.1080/00207543.2015.1056325
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    Citations

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    Cited by:

    1. Prashant Barsing & Yash Daultani & Omkarprasad S. Vaidya & Sushil Kumar, 2018. "Cross-docking Centre Location in a Supply Chain Network: A Social Network Analysis Approach," Global Business Review, International Management Institute, vol. 19(3_suppl), pages 218-234, June.
    2. Liangliang Jin & Qiuhua Tang & Chaoyong Zhang & Xinyu Shao & Guangdong Tian, 2016. "More MILP models for integrated process planning and scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4387-4402, July.
    3. Ma, Jun & Nault, Barrie R. & Tu, Yiliu (Paul), 2023. "Customer segmentation, pricing, and lead time decisions: A stochastic-user-equilibrium perspective," International Journal of Production Economics, Elsevier, vol. 264(C).
    4. Liu, Zugang & Wang, Jia, 2019. "Supply chain network equilibrium with strategic supplier investment: A real options perspective," International Journal of Production Economics, Elsevier, vol. 208(C), pages 184-198.
    5. Muhammad Junaid & Ye Xue & Muzzammil Wasim Syed & Ji Zu Li & Muhammad Ziaullah, 2019. "A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    6. Gaurvendra Singh & Yash Daultani & Rajendra Sahu, 2022. "Investigating the barriers to growth in the Indian food processing sector," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 441-459, June.
    7. 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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