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Simulation-based ripple effect modelling in the supply chain: a network perspective

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  • Yueran Zhang
  • Zhanwen Niu
  • Kaixuan Hou

Abstract

This research investigates the ripple effects of supply chain disruptions across supply chain echelons and identifies vulnerable network characteristics. Utilising simulation methods, the study analyses the impact of disruption scale, propagation extent, and delay on supply chain performance. Employing AnyLogistix software, the study evaluates ripple effects across four different network structures under disruption scenarios resembling the COVID-19 pandemic, characterised by prolonged impacts on supply, demand, logistics, and uncertain recovery times. Comparative analysis across network structures reveals insights into recovery strategies, highlighting the influence of network characteristics on performance outcomes. The findings demonstrate that both disruption severity and propagation delay significantly affect supply chain performance, with high connectivity and betweenness centrality enhancing resilience during prolonged disruptions. This study contributes to the literature by integrating ripple effect assessment with network topology considerations, providing both theoretical insights and practical guidance for optimising supply chain design and management in the post-COVID era. [Submitted: 8 February 2024; Accepted: 3 September 2024]

Suggested Citation

  • Yueran Zhang & Zhanwen Niu & Kaixuan Hou, 2025. "Simulation-based ripple effect modelling in the supply chain: a network perspective," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 20(3), pages 368-410.
    • Handle: RePEc:ids:eujine:v:20:y:2025:i:3:p:368-410
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