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Assessing the vulnerability of supply chains using graph theory

Author

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  • Wagner, Stephan M.
  • Neshat, Nikrouz

Abstract

In today's business environment, harsher and more frequent natural and man-made disasters make supply chains more vulnerable. Supply chain disruptions now seem to occur more frequently and with more serious consequences. During and after supply chain disruptions, companies may lose revenue and incur high recovery costs. If supply chain managers were more capable of measuring and managing supply chain vulnerability, they could reduce the number of disruptions and their impact. In this research we developed an approach based on graph theory to quantify and hence mitigate supply chain vulnerability. Quantification of supply chain vulnerability aids managers in assessing the vulnerability of their supply chains (e.g., across and between supply chains, or over time) and in comparing the effectiveness of different risk mitigation strategies.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:proeco:v:126:y:2010:i:1:p:121-129
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Bates, Samuel & Angeon, Valérie & Ainouche, Ahmed, 2014. "The pentagon of vulnerability and resilience: A methodological proposal in development economics by using graph theory," Economic Modelling, Elsevier, vol. 42(C), pages 445-453.
    2. Denisa MAMILLO, 2015. "Supply Chain Collaboration under Uncertainty in the Albanian Beer Market," Management Dynamics in the Knowledge Economy Journal, College of Management, National University of Political Studies and Public Administration, vol. 3(1), pages 99-117, March.
    3. Fan, Huan & Li, Gang & Sun, Hongyi & Cheng, T.C.E., 2017. "An information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences," International Journal of Production Economics, Elsevier, vol. 185(C), pages 63-75.
    4. Shao, Xiao-Feng, 2012. "Demand-side reactive strategies for supply disruptions in a multiple-product system," International Journal of Production Economics, Elsevier, vol. 136(1), pages 241-252.
    5. Chowdhury, Md. Maruf Hossan & Quaddus, Mohammed A., 2015. "A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh☆,☆☆☆This manuscript was pro," Omega, Elsevier, vol. 57(PA), pages 5-21.
    6. repec:spr:eurjtl:v:7:y:2018:i:1:d:10.1007_s13676-017-0108-5 is not listed on IDEAS
    7. repec:gam:jsusta:v:10:y:2018:i:9:p:3099-:d:166793 is not listed on IDEAS
    8. repec:beo:journl:v:62:y:2017:i:214:p:121-138 is not listed on IDEAS
    9. Ancarani, A. & Di Mauro, C. & D'Urso, D., 2013. "A human experiment on inventory decisions under supply uncertainty," International Journal of Production Economics, Elsevier, vol. 142(1), pages 61-73.
    10. repec:eee:jomega:v:75:y:2018:i:c:p:165-181 is not listed on IDEAS
    11. Klibi, Walid & Martel, Alain, 2012. "Modeling approaches for the design of resilient supply networks under disruptions," International Journal of Production Economics, Elsevier, vol. 135(2), pages 882-898.
    12. repec:kap:netspa:v:18:y:2018:i:1:d:10.1007_s11067-017-9370-1 is not listed on IDEAS
    13. Salehi Sadghiani, N. & Torabi, S.A. & Sahebjamnia, N., 2015. "Retail supply chain network design under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 95-114.
    14. Zhao, Chunfu & Chen, Bin, 2014. "China’s oil security from the supply chain perspective: A review," Applied Energy, Elsevier, vol. 136(C), pages 269-279.

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