IDEAS home Printed from https://ideas.repec.org/a/bla/jscmgt/v60y2024i3p18-38.html
   My bibliography  Save this article

Cascading disruptions: Impact of modularity and nexus supplier predictions

Author

Listed:
  • Jafar Namdar
  • Jennifer Blackhurst
  • Kang Zhao
  • Suyong Song

Abstract

Disruptions can start at one supplier in the supply network and ripple through, impacting other suppliers and firms, known as cascading disruptions. This research analyzes the effect of supply network modularity on cascading disruptions. Modularity measures the degree to which a supply network can be divided into self‐contained sub‐networks and has different effects on supply network resilience. A highly modular supply network prevents cascading disruptions from spreading through the whole network because of the lack of bridges between modules (lack of inter‐module connectivity). Hence, the size of cascading disruptions—measured by the number of suppliers impacted by a cascading disruption—in highly modular supply networks tends to be smaller than the size of cascading disruptions in less modular supply networks. However, the high level of internal connectivity within a module (excessive intra‐module connectivity) acts as an incubator for cascading disruptions. This means a small disruption in a modular network may impact fewer suppliers (i.e., smaller cascading size) but with higher severity measured by service level. Finally, building upon the theoretical concept of nexus suppliers, this research proposes a new predictive model to identify the operational nexus suppliers whose disruptions would considerably impact focal firms' operations. The model's accuracy is empirically tested on real‐world global supply networks involving 2598 unique firms and suppliers across 51 countries and 111 industries. The model identifies nexus suppliers with 95% accuracy, allowing managers and policymakers to plan for mitigation strategies proactively.

Suggested Citation

  • Jafar Namdar & Jennifer Blackhurst & Kang Zhao & Suyong Song, 2024. "Cascading disruptions: Impact of modularity and nexus supplier predictions," Journal of Supply Chain Management, Institute for Supply Management, vol. 60(3), pages 18-38, July.
  • Handle: RePEc:bla:jscmgt:v:60:y:2024:i:3:p:18-38
    DOI: 10.1111/jscm.12326
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jscm.12326
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jscm.12326?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
    ---><---

    References listed on IDEAS

    as
    1. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    2. Mahmut Yasar & Carl H. Nelson & Roderick Rejesus, 2006. "Productivity and Exporting Status of Manufacturing Firms: Evidence from Quantile Regressions," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 142(4), pages 675-694, December.
    3. 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.
    4. 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.
    5. Ying Rong & Lawrence V. Snyder & Zuo‐Jun Max Shen, 2017. "Bullwhip and reverse bullwhip effects under the rationing game," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(3), pages 203-216, April.
    6. Moritz Laber & Peter Klimek & Martin Bruckner & Liuhuaying Yang & Stefan Thurner, 2022. "Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability," Papers 2210.01846, arXiv.org, revised Jun 2023.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Owen Q. Wu & Hong Chen, 2010. "Optimal Control and Equilibrium Behavior of Production-Inventory Systems," Management Science, INFORMS, vol. 56(8), pages 1362-1379, August.
    9. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    10. Li, Yuhong & Chen, Kedong & Collignon, Stephane & Ivanov, Dmitry, 2021. "Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability," European Journal of Operational Research, Elsevier, vol. 291(3), pages 1117-1131.
    11. 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).
    12. Tingting Yan & Thomas Y. Choi & Yusoon Kim & Yang Yang, 2015. "A Theory of the Nexus Supplier: A Critical Supplier From A Network Perspective," Journal of Supply Chain Management, Institute for Supply Management, vol. 51(1), pages 52-66, January.
    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. William Schueller & Christian Diem & Melanie Hinterplattner & Johannes Stangl & Beate Conrady & Markus Gerschberger & Stefan Thurner, 2022. "Propagation of disruptions in supply networks of essential goods: A population-centered perspective of systemic risk," Papers 2201.13325, arXiv.org.
    2. 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.
    3. Manupati, V.K. & Schoenherr, Tobias & Ramkumar, M. & Panigrahi, Suraj & Sharma, Yash & Mishra, Prakriti, 2022. "Recovery strategies for a disrupted supply chain network: Leveraging blockchain technology in pre- and post-disruption scenarios," International Journal of Production Economics, Elsevier, vol. 245(C).
    4. 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).
    5. Zhimei Lei & Li Cui & Jing Tang & Lujie Chen & Bingbing Liu, 2024. "Supply chain resilience in the context of I4.0 and I5.0 from a multilayer network ripple effect perspective," Annals of Operations Research, Springer, vol. 342(2), pages 1149-1192, November.
    6. Niels Bugert & Rainer Lasch, 2023. "Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks," Journal of Business Economics, Springer, vol. 93(5), pages 859-889, July.
    7. Dass, Mayukh & Reshadi, Mehrnoosh & Li, Yuewu, 2023. "An exploration of ripple effects of advertising among major suppliers in a supply chain network," Journal of Business Research, Elsevier, vol. 169(C).
    8. Brusset, Xavier & Ivanov, Dmitry & Jebali, Aida & La Torre, Davide & Repetto, Marco, 2023. "A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic," International Journal of Production Economics, Elsevier, vol. 263(C).
    9. Giovanna Culot & Matteo Podrecca & Guido Nassimbeni & Guido Orzes & Marco Sartor, 2023. "Using supply chain databases in academic research: A methodological critique," Journal of Supply Chain Management, Institute for Supply Management, vol. 59(1), pages 3-25, January.
    10. Duschl, Matthias & Schimke, Antje & Brenner, Thomas & Luxen, Dennis, 2011. "Firm growth and the spatial impact of geolocated external factors: Empirical evidence for German manufacturing firms," Working Paper Series in Economics 36, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    11. Hendrik Schmitz & Reinhard Madlener, 2020. "Heterogeneity in price responsiveness for residential space heating in Germany," Empirical Economics, Springer, vol. 59(5), pages 2255-2281, November.
    12. Alex Coad, 2007. "A Closer Look at Serial Growth Rate Correlation," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 31(1), pages 69-82, August.
    13. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    14. Joachim Wagner, 2014. "Exports, foreign direct investments and productivity: are services firms different?," The Service Industries Journal, Taylor & Francis Journals, vol. 34(1), pages 24-37, January.
    15. Svetlana Batrakova & Ronald Davies, 2012. "Is there an environmental benefit to being an exporter? Evidence from firm-level data," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 148(3), pages 449-474, September.
    16. Kettani, Maryème & Sanin, Maria Eugenia, 2024. "Energy consumption and energy poverty in Morocco," Energy Policy, Elsevier, vol. 185(C).
    17. Wagner Joachim & Schank Thorsten & Schnabel Claus & Addison John T., 2006. "Works Councils, Labor Productivity and Plant Heterogeneity: First Evidence from Quantile Regressions," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(5), pages 505-518, October.
    18. Wei Xu & Dong-Ping Song, 2022. "Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies," Operational Research, Springer, vol. 22(3), pages 2343-2371, July.
    19. Scarpin, Marcia Regina Santiago & Scarpin, Jorge Eduardo & Krespi Musial, Nayane Thais & Nakamura, Wilson Toshiro, 2022. "The implications of COVID-19: Bullwhip and ripple effects in global supply chains," International Journal of Production Economics, Elsevier, vol. 251(C).
    20. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).

    More about this item

    Statistics

    Access and download statistics

    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:bla:jscmgt:v:60:y:2024:i:3:p:18-38. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1523-2409 .

    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.