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Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains

Author

Listed:
  • Alexander Pavlov

    (Mozhaisky Aerospace Academy)

  • Dmitry Ivanov

    (Berlin School of Economics and Law)

  • Frank Werner

    (Otto-von-Guericke University Magdeburg)

  • Alexandre Dolgui

    (IMT Atlantique)

  • Boris Sokolov

    (Saint Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS))

Abstract

The studies on supply chain (SC) disruption management frequently assume the existence of some negative scenarios and suggest ways to proactively protect and reactively recover the SC operations and performance if such scenarios occur. Though, there is a paucity of research on how to support methodologically the detection of realistic disruption scenarios, ideally of different risk aversion degrees. The contribution of our study lies in a conceptualization of a new methodical approach to the detection of disruption scenarios, ripple effect dispersal and recovery paths in supply chains on the basis of structural genomes. The objective is to integrate and expand the existing knowledge gained isolated in robustness analysis and recovery planning into a comprehensive framework for building a theory as well as for managerial purposes. The outcomes of this research constitute a useful decision-making support tool that allows detecting disruption scenarios at different risk-aversion levels based on the quantification of the structural robustness with the use of the genome method and observing the scope of disruption propagation, i.e., the ripple effect. The advantage of using a robustness computation by the genome method is that this allows detecting both the disruption scenarios of different severity, the ripple effect dispersal, and the corresponding recovery paths. Our results can be of value for decision-makers to compare different supply chain structural designs regarding the robustness and to identify disruption scenarios that interrupt the supply chain operations to different extents. The scenario detection can be further used for identifying optimal reconfiguration paths to deploy proactive contingency and reactive recovery policies. We show a correlation between the risk aversion degree of disruption scenarios and the outcomes of the reconfiguration policies.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:319:y:2022:i:1:d:10.1007_s10479-019-03454-1
    DOI: 10.1007/s10479-019-03454-1
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    References listed on IDEAS

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    1. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
    2. Tingting Cui & Yanfeng Ouyang & Zuo-Jun Max Shen, 2010. "Reliable Facility Location Design Under the Risk of Disruptions," Operations Research, INFORMS, vol. 58(4-part-1), pages 998-1011, August.
    3. Dmitry Ivanov & Alexandre Dolgui, 2019. "Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 5119-5136, August.
    4. Tadeusz Sawik, 2017. "A portfolio approach to supply chain disruption management," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1970-1991, April.
    5. Wilson, Martha C., 2007. "The impact of transportation disruptions on supply chain performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(4), pages 295-320, July.
    6. Mishra, Deepa & Sharma, R.R.K. & Kumar, Sameer & Dubey, Rameshwar, 2016. "Bridging and buffering: Strategies for mitigating supply risk and improving supply chain performance," International Journal of Production Economics, Elsevier, vol. 180(C), pages 183-197.
    7. Eugene Levner & Alexander Ptuskin, 2018. "Entropy-based model for the ripple effect: managing environmental risks in supply chains," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2539-2551, April.
    8. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    9. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    10. Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao & Chih-Ching Yeh, 2017. "System reliability for a multistate intermodal logistics network with time windows," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1957-1969, April.
    11. Yossi Sheffi, 2005. "The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262693496, April.
    12. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2018. "Ripple effect in the supply chain: an analysis and recent literature," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 414-430, January.
    13. Dmitry Ivanov & Alexandre Dolgui & Ajay Das & Boris Sokolov, 2019. "Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility," International Series in Operations Research & Management Science, in: Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov (ed.), Handbook of Ripple Effects in the Supply Chain, pages 309-332, Springer.
    14. 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.
    15. Cavalcante, Ian M. & Frazzon, Enzo M. & Forcellini, Fernando A. & Ivanov, Dmitry, 2019. "A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing," International Journal of Information Management, Elsevier, vol. 49(C), pages 86-97.
    16. Kevin P. Scheibe & Jennifer Blackhurst, 2018. "Supply chain disruption propagation: a systemic risk and normal accident theory perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 43-59, January.
    17. Dmitry Ivanov & Alexander Pavlov & Boris Sokolov, 2016. "Exact and heuristic methods for integrated supply chain design reliability analysis," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 10(2), pages 206-224.
    18. Boris Sokolov & Dmitry Ivanov & Alexandre Dolgui & Alexander Pavlov, 2016. "Structural quantification of the ripple effect in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 152-169, January.
    19. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    20. John R. Macdonald & Christopher W. Zobel & Steven A. Melnyk & Stanley E. Griffis, 2018. "Supply chain risk and resilience: theory building through structured experiments and simulation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(12), pages 4337-4355, June.
    21. Sanjoy Kumar Paul & Ruhul Sarker & Daryl Essam & Paul Tae-Woo Lee, 2019. "A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain," Annals of Operations Research, Springer, vol. 280(1), pages 299-335, September.
    22. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    23. Cui, Tingting & Ouyang, Yanfeng & Shen, Zuo-Jun Max J, 2010. "Reliable Facility Location Design under the Risk of Disruptions," University of California Transportation Center, Working Papers qt5sh2c7pw, University of California Transportation Center.
    24. Ivanov, Dmitry & Pavlov, Alexander & Sokolov, Boris, 2014. "Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics," European Journal of Operational Research, Elsevier, vol. 237(2), pages 758-770.
    25. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    26. Dmitry Ivanov, 2018. "Structural Dynamics and Resilience in Supply Chain Risk Management," International Series in Operations Research and Management Science, Springer, number 978-3-319-69305-7, April.
    27. Hosseini, Seyedmohsen & Morshedlou, Nazanin & Ivanov, Dmitry & Sarder, M.D. & Barker, Kash & Khaled, Abdullah Al, 2019. "Resilient supplier selection and optimal order allocation under disruption risks," International Journal of Production Economics, Elsevier, vol. 213(C), pages 124-137.
    28. Rezapour, Shabnam & Farahani, Reza Zanjirani & Pourakbar, Morteza, 2017. "Resilient supply chain network design under competition: A case study," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1017-1035.
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