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Quantification of risk mitigation environment of supply chains using graph theory and matrix methods

Author

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  • Mohd Nishat Faisal
  • D.K. Banwet
  • Ravi Shankar

Abstract

Today supply chains leverage their partner's competencies and in the process also inherit the risks associated with various links of a supply chain. Although it is impossible to completely eliminate various risks, an environment can be created which helps to effectively mitigate risk. The most difficult part of Supply Chain Risk Management (SCRM) is an understanding of the variables related to risk mitigation and their relative interdependencies. This paper presents a conceptual framework which models various variables associated with risk mitigation environment along with their interdependencies. Using graph theory and matrix methods, the Risk Mitigation Environment (RME) is quantified and presented in the form of a single numerical index. The proposed model is quite versatile from the point of view that it provides an opportunity to integrate new variables which could impact the overall supply chain risk mitigation environment along with the potential to benchmark supply chains on risk mitigation dimension.

Suggested Citation

  • Mohd Nishat Faisal & D.K. Banwet & Ravi Shankar, 2007. "Quantification of risk mitigation environment of supply chains using graph theory and matrix methods," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 1(1), pages 22-39.
    • Handle: RePEc:ids:eujine:v:1:y:2007:i:1:p:22-39
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    Citations

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    Cited by:

    1. Bhupender Singh & Sandeep Grover & Vikram Singh, 2017. "A benchmarking model for Indian service industries using MICMAC and WISM approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1266-1281, November.
    2. Wagner, Stephan M. & Neshat, Nikrouz, 2010. "Assessing the vulnerability of supply chains using graph theory," International Journal of Production Economics, Elsevier, vol. 126(1), pages 121-129, July.
    3. Bingsheng Liu & Tengfei Huo & Pinchao Liao & Jie Gong & Bin Xue, 2015. "A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling," Group Decision and Negotiation, Springer, vol. 24(5), pages 855-883, September.
    4. V. R. Pramod & D. K. Banwet & P. R. S. Sarma, 2016. "Understanding the barriers of service supply chain management: an exploratory case study from Indian telecom industry," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 358-374, June.
    5. Nikhil Dev & Rajesh Kumar Attri, 2017. "Evaluation of gas turbine power plant efficiency using graph theoretic approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 676-689, November.
    6. Marcus V. C. Fagundes & Bernd Hellingrath & Francisco G. M. Freires, 2021. "Supplier Selection Risk: A New Computer-Based Decision-Making System with Fuzzy Extended AHP," Logistics, MDPI, vol. 5(1), pages 1-17, March.

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