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

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  • 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
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    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. Liu, Hui & Su, Bingbing & Guo, Min & Wang, Jingbei, 2024. "Exploring R&D network resilience under risk propagation: An organizational learning perspective," International Journal of Production Economics, Elsevier, vol. 273(C).
    5. 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).

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