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An analytical methodological framework for the optimal design of resilient supply chains

Author

Listed:
  • Eleftherios Iakovou
  • Dimitrios Vlachos
  • Anastasios Xanthopoulos

Abstract

Managing risk and disruptions have emerged as issues of critical importance for today's globalised supply chains. In this work, we first provide an up-to-date taxonomy of the risks that supply chains are exposed to along with the appropriate solutions that can be employed to improve their resiliency. We then proceed by outlining an analytical methodological framework for supporting the design and operations of efficient supply chains in the new business environment by taking into account the stochasticity of various factors that can lead to disruptions. More specifically, we present a novel, to our knowledge, stochastic single period quantitative model that can assist in the decision-making process regarding the optimal security protection-based configuration of a supply chain both for single and multiple disruption cases. We obtain closed-form solutions in all cases allowing for capturing the optimal trade-off between inventory policies and supply chain protection levels. Finally, we discuss additional useful managerial insights that were obtained.

Suggested Citation

  • Eleftherios Iakovou & Dimitrios Vlachos & Anastasios Xanthopoulos, 2007. "An analytical methodological framework for the optimal design of resilient supply chains," International Journal of Logistics Economics and Globalisation, Inderscience Enterprises Ltd, vol. 1(1), pages 1-20.
    • Handle: RePEc:ids:injleg:v:1:y:2007:i:1:p:1-20
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    Citations

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    Cited by:

    1. Güller, Mustafa & Koc, Emre & Henke, Michael & Noche, Bernd & Hingst, Lennart, 2015. "A Simulation-based Analysis of Supply Chain Resilience," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 533-556, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Annarelli, Alessandro & Nonino, Fabio, 2016. "Strategic and operational management of organizational resilience: Current state of research and future directions," Omega, Elsevier, vol. 62(C), pages 1-18.
    3. Chen, Liwei & Dui, Hongyan & Zhang, Chi, 2020. "A resilience measure for supply chain systems considering the interruption with the cyber-physical systems," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Cardoso, Sónia R. & Paula Barbosa-Póvoa, Ana & Relvas, Susana & Novais, Augusto Q., 2015. "Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty," Omega, Elsevier, vol. 56(C), pages 53-73.
    5. Chih-Hung Hsu & An-Yuan Chang & Ting-Yi Zhang & Wei-Da Lin & Wan-Ling Liu, 2021. "Deploying Resilience Enablers to Mitigate Risks in Sustainable Fashion Supply Chains," Sustainability, MDPI, vol. 13(5), pages 1-24, March.
    6. 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.
    7. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.

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