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Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts

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  • Bueno-Solano, Alfredo
  • Cedillo-Campos, Miguel Gastón

Abstract

Understanding disruptions and how their effects propagate through the supply chain is critical to promote security and efficient movement of goods. This research proposes a system dynamics model as an effective quantitative approach for analyzing the effects of the materialization and simultaneous propagation of disruptions produced by terrorist acts on global supply chains performance. The article shows that the impact on inventory levels in the supply chain can increase 600% compared to normal operating conditions as a result of increasing the security measures on international borders. Finally, useful conclusions for designing more resilient supply chains and future research are exposed.

Suggested Citation

  • Bueno-Solano, Alfredo & Cedillo-Campos, Miguel Gastón, 2014. "Dynamic impact on global supply chains performance of disruptions propagation produced by terrorist acts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 1-12.
    • Handle: RePEc:eee:transe:v:61:y:2014:i:c:p:1-12
    DOI: 10.1016/j.tre.2013.09.005
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    Cited by:

    1. A. V. Thomas & Biswajit Mahanty, 2021. "Dynamic assessment of control system designs of information shared supply chain network experiencing supplier disruption," Operational Research, Springer, vol. 21(1), pages 425-451, March.
    2. Dmitry Ivanov, 2017. "Simulation-based ripple effect modelling in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 2083-2101, April.
    3. Cedillo-Campos, Miguel Gastón & Lizarraga-Lizarraga, Giovanni & Martner-Peyrelongue, Carlos Daniel, 2017. "MiF3 method: Modeling intermodal fluidity freight flows," Research in Transportation Economics, Elsevier, vol. 61(C), pages 15-24.
    4. Lai, Xinfeng & Chen, Zhixiang & Wang, Xin & Chiu, Chun-Hung, 2023. "Risk propagation and mitigation mechanisms of disruption and trade risks for a global production network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    5. Iftikhar, Ilaria Giannoccaro & Anas, 2023. "Mitigating ripple effect in supply networks: the effect of trust and topology on resilience," OSF Preprints 2spt3, Center for Open Science.
    6. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    7. Ernesto A. Lagarda-Leyva & María Paz Guadalupe Acosta-Quintana & Javier Portugal-Vásquez & Arnulfo A. Naranjo-Flores & Alfredo Bueno-Solano, 2023. "System Dynamics and Sustainable Solution: The Case in a Large-Scale Pallet Manufacturing Company," Sustainability, MDPI, vol. 15(15), pages 1-21, July.
    8. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications," International Journal of Production Economics, Elsevier, vol. 232(C).
    9. Mohamed El Abdellaoui & Gilles Pache, 2019. "Effects of disruptive events within the supply chain on perceived logistics performance," Economics Bulletin, AccessEcon, vol. 39(1), pages 41-54.
    10. Chen, Li-Ming & Liu, Yan Emma & Yang, Shu-Jung Sunny, 2015. "Robust supply chain strategies for recovering from unanticipated disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 198-214.
    11. Yang, Yuefeng & Xu, Xuerong, 2015. "Post-disaster grain supply chain resilience with government aid," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 76(C), pages 139-159.
    12. Niknejad, Ali & Petrovic, Dobrila, 2016. "A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks," International Journal of Production Economics, Elsevier, vol. 179(C), pages 44-58.
    13. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    14. Tianjian Yang & Weiguo Fan, 2016. "Information management strategies and supply chain performance under demand disruptions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 8-27, January.
    15. Tortorella, Guilherme L. & Fogliatto, Flavio S. & Saurin, Tarcísio A. & Tonetto, Leandro M. & McFarlane, Duncan, 2022. "Contributions of Healthcare 4.0 digital applications to the resilience of healthcare organizations during the COVID-19 outbreak," Technovation, Elsevier, vol. 111(C).
    16. Balster, Andreas & Friedrich, Hanno, 2019. "Dynamic freight flow modelling for risk evaluation in food supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 4-22.
    17. Cheshmberah Mohsen, 2020. "Developing an Integrated Framework for Supplier Evaluation based on Relevant Attributes and Performance Measures," Logistics, Supply Chain, Sustainability and Global Challenges, Sciendo, vol. 11(1), pages 101-113, February.
    18. Cedillo-Campos, Miguel Gastón & Piña-Barcenas, Jared & Pérez-González, Carlos Mario & Mora-Vargas, Jaime, 2022. "How to measure and monitor the transportation infrastructure contribution to logistics value of supply chains?," Transport Policy, Elsevier, vol. 120(C), pages 120-129.

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