IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v228y2020ics0925527320300840.html
   My bibliography  Save this article

Exploring supply chain network resilience in the presence of the ripple effect

Author

Listed:
  • Li, Yuhong
  • Zobel, Christopher W.

Abstract

This study aims to investigate overall supply chain network resilience (SCNR) in the presence of ripple effect, or risk propagation, i.e. the phenomenon that disruptions at a few firms in a supply chain network (SCN) can spread to their neighboring firms, then eventually spread to other firms in the SCN. We begin by developing a multi-dimensional quantitative framework to measure SCNR, which includes three resilience dimensions based on three different network performance indicators. Given this framework, we then systematically explore the determining factors of SCNR and present a comprehensive analysis of how network structure and node risk capacity influence different aspects of SCNR. Our results clearly indicate the following important implications for managers. First, the influence of network type on SCNR tends to be more significant in the short-term than it is in the longer-term, given the ripple effect. Second, SCNR can be improved more effectively by enhancing node risk capacity than by adjusting network structure. Third, tradeoffs exist between the robustness of the network against a disruption and its ability to recover from that disruption. Fourth, different network performance indicators can provide different perspectives on SCNR. Together these help show that the multi-dimensional framework enables a better characterization of the complexity of SCNR, and thus that it provides support for more informed managerial decision-making about investing in improving resilience. The paper concludes the discussion by addressing opportunities for further extending the research effort.

Suggested Citation

  • Li, Yuhong & Zobel, Christopher W., 2020. "Exploring supply chain network resilience in the presence of the ripple effect," International Journal of Production Economics, Elsevier, vol. 228(C).
  • Handle: RePEc:eee:proeco:v:228:y:2020:i:c:s0925527320300840
    DOI: 10.1016/j.ijpe.2020.107693
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320300840
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2020.107693?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Dmitry Ivanov, 2018. "Supply Chain Risk Management: Bullwhip Effect and Ripple Effect," International Series in Operations Research & Management Science, in: Structural Dynamics and Resilience in Supply Chain Risk Management, chapter 0, pages 19-44, Springer.
    3. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    4. Jalili, Mahdi, 2011. "Error and attack tolerance of small-worldness in complex networks," Journal of Informetrics, Elsevier, vol. 5(3), pages 422-430.
    5. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    6. Brian Tomlin, 2006. "On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks," Management Science, INFORMS, vol. 52(5), pages 639-657, May.
    7. Brusset, Xavier & Teller, Christoph, 2017. "Supply chain capabilities, risks, and resilience," International Journal of Production Economics, Elsevier, vol. 184(C), pages 59-68.
    8. Jan W. Rivkin & Nicolaj Siggelkow, 2007. "Patterned Interactions in Complex Systems: Implications for Exploration," Management Science, INFORMS, vol. 53(7), pages 1068-1085, July.
    9. Craig R. Carter & Dale S. Rogers & Thomas Y. Choi, 2015. "Toward the Theory of the Supply Chain," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(2), pages 89-97, April.
    10. 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.
    11. Hayato Goto & Hideki Takayasu & Misako Takayasu, 2017. "Estimating risk propagation between interacting firms on inter-firm complex network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-12, October.
    12. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    13. Nobuyuki Hanaki & Alexander Peterhansl & Peter S. Dodds & Duncan J. Watts, 2007. "Cooperation in Evolving Social Networks," Management Science, INFORMS, vol. 53(7), pages 1036-1050, July.
    14. Emma Brandon-Jones & Brian Squire & Chad W. Autry & Kenneth J. Petersen, 2014. "A Contingent Resource-Based Perspective of Supply Chain Resilience and Robustness," Journal of Supply Chain Management, Institute for Supply Management, vol. 50(3), pages 55-73, July.
    15. 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.
    16. 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.
    17. Nagurney, Anna, 2010. "Optimal supply chain network design and redesign at minimal total cost and with demand satisfaction," International Journal of Production Economics, Elsevier, vol. 128(1), pages 200-208, November.
    18. Thomas J. Holmes, 2011. "The Diffusion of Wal‐Mart and Economies of Density," Econometrica, Econometric Society, vol. 79(1), pages 253-302, January.
    19. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
    20. 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, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    2. Li, Yuhong & Zobel, Christopher W. & Seref, Onur & Chatfield, Dean, 2020. "Network characteristics and supply chain resilience under conditions of risk propagation," International Journal of Production Economics, Elsevier, vol. 223(C).
    3. 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).
    4. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    5. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    6. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    7. Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    8. 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.
    9. Garvey, Myles D. & Carnovale, Steven, 2020. "The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation," International Journal of Production Economics, Elsevier, vol. 228(C).
    10. 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.
    11. Lohmer, Jacob & Bugert, Niels & Lasch, Rainer, 2020. "Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study," International Journal of Production Economics, Elsevier, vol. 228(C).
    12. 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).
    13. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    14. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    15. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    16. Vimal K.E.K & Simon Peter Nadeem & Mahadharsan Ravichandran & Manavalan Ethirajan & Jayakrishna Kandasamy, 2022. "Resilience strategies to recover from the cascading ripple effect in a copper supply chain through project management," Operations Management Research, Springer, vol. 15(1), pages 440-460, June.
    17. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.
    18. 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.
    19. Benjamin Korder & Julien Maheut & Matthias Konle, 2024. "Simulation Methods and Digital Strategies for Supply Chains Facing Disruptions: Insights from a Systematic Literature Review," Sustainability, MDPI, vol. 16(14), pages 1-31, July.
    20. Meghan Stewart & Dmitry Ivanov, 2022. "Design redundancy in agile and resilient humanitarian supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 633-659, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:228:y:2020:i:c:s0925527320300840. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.