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Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches

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  • R. Rajesh
  • V. Ravi
  • R. Venkata Rao

Abstract

Supply chains are becoming more lengthy and complex due to globalisation and vertical integrations. In this context, adopting proactive approaches is needed for dealing with changing risks and vulnerabilities for securing supply chain systems. Supply chain risks are interlinked and thus, one mitigation strategy can reduce many of other supply chain risks. For example, aggregate or pooling demand reduces forecast risks, capacity risks and inventory risks. Also, some of the risk mitigation strategies have negative influences over certain supply chain risks as adding capacity has a negative influence on capacity risks. Twelve major supply chain risk categories and 21 risk mitigation strategies with typical focus on electronics manufacturing supply chains have been identified. A combination of grey theory and digraph-matrix methodologies has been used for quantifying various supply chain risk mitigation strategies and this approach is not seen in literature till date. The proposed model was also tested taking a case study of an Indian electronics manufacturing company. Obtained results were also subject to sensitivity analysis. The net positive influence values of risk mitigation strategies proposed in this research could effectively be used by top management for ascertaining their risk mitigation strategies for better management of supply chains as a whole.

Suggested Citation

  • R. Rajesh & V. Ravi & R. Venkata Rao, 2015. "Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 53(1), pages 238-257, January.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:1:p:238-257
    DOI: 10.1080/00207543.2014.948579
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    Cited by:

    1. Che-Jung Chang & Jan-Yan Lin & Peng Jin, 2017. "A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 409-422, September.
    2. V. V. Brinza & Yu. Yu. Kostyukhin & I. V. Fadeeva, 2017. "Potential of modeling techniques organizational systems with matrix structure and the possibility of expanding their information base," Russian Journal of Industrial Economics, MISIS, issue 3.
    3. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    4. Rajesh, R., 2017. "Technological capabilities and supply chain resilience of firms: A relational analysis using Total Interpretive Structural Modeling (TISM)," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 161-169.
    5. Govindan, Kannan & Chaudhuri, Atanu, 2016. "Interrelationships of risks faced by third party logistics service providers: A DEMATEL based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 177-195.
    6. Fulzele, Vijayta & Shankar, Ravi, 2022. "Improving freight transportation performance through sustainability best practices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 285-299.
    7. Xu Jing & Yao Guanxin & Dai Panqian, 2020. "Quality Decision-Making Behavior of Bodies Participating in the Agri-Foods E-Supply Chain," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    8. 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.

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