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Assessing supply chain robustness to links failure

Author

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  • Belarmino Adenso-Díaz
  • Julio Mar-Ortiz
  • Sebastián Lozano

Abstract

Supply chain networks need to respond efficiently to operation disruptions, as one of their aims is to guarantee the on time delivery of products. Hence, robustness has become one of the important issues to consider when designing supply networks. There are alternative ways to measure what robustness means in this context. In this paper, we propose a new metric based on the effect on service level of the collapse of active transportation links. Numerical experiments are carried out to understand how different design factors affect robustness. Robustness under a targeted attack is compared with robustness to random failures. Results show that flow complexity (i.e. the number of potential transportation links between supply network nodes) is the most influential factor affecting supply network and its robustness, as well as the service level that can be maintained after disruptions. Thus, diversification both in supply sources and transportation routes seems to be key to robustness.

Suggested Citation

  • Belarmino Adenso-Díaz & Julio Mar-Ortiz & Sebastián Lozano, 2018. "Assessing supply chain robustness to links failure," International Journal of Production Research, Taylor & Francis Journals, vol. 56(15), pages 5104-5117, August.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:15:p:5104-5117
    DOI: 10.1080/00207543.2017.1419582
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    Citations

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    Cited by:

    1. Gast, Johannes & Kirkach, Evelina & Ivanov, Dmitry, 2022. "Structured literature review of transport networks and Supply Chain Resilience," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 469-496, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
    3. Nathalie Fabbe-Costes & Yasmina Ziad, 2021. "Improving Supply Chain Robustness & Resilience? Lessons from a case study in the automotive industry during the first wave of Covid-19," Post-Print hal-03306223, HAL.
    4. Wang, Jiepeng & Zhou, Hong & Sun, Xinlei & Yuan, Yufei, 2023. "A novel supply chain network evolving model under random and targeted disruptions," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    5. Jiakuan Chen & Haoyu Wen, 2023. "The application of complex network theory for resilience improvement of knowledge-intensive supply chains," Operations Management Research, Springer, vol. 16(3), pages 1140-1161, September.
    6. Nicolas Fröhlich & Stefan Ruzika, 2022. "Interdicting facilities in tree networks," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 95-118, April.
    7. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Xiaojin Yuan & Chunbao Ge & Yapan Liu & Na Li & Ying Wang, 2022. "Evolution of Global Crude Oil Trade Network Structure and Resilience," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    9. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.

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