IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v59y2021i1p325-341.html
   My bibliography  Save this article

The relationship between nested patterns and the ripple effect in complex supply networks

Author

Listed:
  • Vinod Kumar Chauhan
  • Supun Perera
  • Alexandra Brintrup

Abstract

Supply networks (SNs) play a vital role in fuelling trade and economic growth. Due to their interconnectedness, firm-level disruptions can cause perturbations to ripple through SNs, magnifying initial impact. Contemporary research on ripple effects focussed on understanding various structural features of SNs to predict and control disruption propagation. Our work adds to this body of knowledge by analysing an intriguing topological property that emerges in SNs: ‘nestedness’, which is defined as a pattern of organisation where products that are supplied by specialist suppliers are a subset of products that are supplied by generalist suppliers. In other words, generalists are also specialists. While previous research examined the emergence of nestedness and its possible reasons, its relationship to SN resilience remained unknown. Here, we develop a cascade model by bringing together the product-supplier-buyer structure; which provides us with fine-grained information on SN dependencies. We simulate disruptions in nested and non-nested organisations of the global automotive SN, and find that nested organisations are significantly more robust to random disruptions but vulnerable to hub disruptions under cascade conditions. However, nested structures are not as resilient; as they do not benefit from a response strategy where buyers seek alternative suppliers; because alternative suppliers do not exist. On the other hand, randomly connected SNs are vulnerable to cascades but can allow network reconfiguration.

Suggested Citation

  • Vinod Kumar Chauhan & Supun Perera & Alexandra Brintrup, 2021. "The relationship between nested patterns and the ripple effect in complex supply networks," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 325-341, January.
  • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:1:p:325-341
    DOI: 10.1080/00207543.2020.1831096
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2020.1831096
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2020.1831096?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dirzka, Christopher & Acciaro, Michele, 2022. "Global shipping network dynamics during the COVID-19 pandemic's initial phases," Journal of Transport Geography, Elsevier, vol. 99(C).
    2. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    3. Shi, Xiaoqiu & Long, Wei & Li, Yanyan & Deng, Dingshan, 2022. "Robustness of interdependent supply chain networks against both functional and structural cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    4. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(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:taf:tprsxx:v:59:y:2021:i:1:p:325-341. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.