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Risk propagation in supply-chain network considering node heterogeneity

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  • Chen, Yucheng
  • Xia, Yongxiang
  • Hua, Zhen

Abstract

In the current highly interconnected global economy, risk propagation within supply-chain networks has drawn significant attention from researchers because of its profound impact. Given the varying risk-propagation capabilities of different firms within the supply chain, we propose a risk-propagation model that considers the heterogeneity between nodes, referred to as the Degree-Dependent Risk Propagation (DDRP) model. We analyze the effects of different heterogeneity parameters on the performance of risk propagation in the supply-chain network and further explore how these effects influence the efficiency of logistics within the supply-chain network. The results indicate that the heterogeneity between nodes significantly increases the vulnerability of the supply-chain network, making it less efficient when facing risk propagation. In a highly heterogeneous network, more nodes become infected, leading to a notable decline in logistics-transportation efficiency, which severely disrupts the normal functioning of the entire supply chain. Our research not only provides a novel theoretical model for risk propagation in supply-chain networks, but also offers valuable practical insights for managers and decision-makers. By identifying and understanding the influence of heterogeneity on risk propagation, decision-makers can formulate more effective risk-management strategies, thereby enhancing supply-chain resilience and efficiency.

Suggested Citation

  • Chen, Yucheng & Xia, Yongxiang & Hua, Zhen, 2025. "Risk propagation in supply-chain network considering node heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 657(C).
    • Handle: RePEc:eee:phsmap:v:657:y:2025:i:c:s0378437124007453
    DOI: 10.1016/j.physa.2024.130236
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    1. Liu, Chong & Tang, Jiaze & Zhang, Zhi-Hai, 2024. "Impacts of capacity redundancy and process flexibility on risk mitigation in e-waste recycling supply chain management," Omega, Elsevier, vol. 128(C).
    2. Sean P. Willems, 2008. "Data Set--Real-World Multiechelon Supply Chains Used for Inventory Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 19-23, February.
    3. 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.
    4. 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.
    5. 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).
    6. Sharma, Amalesh & Adhikary, Anirban & Borah, Sourav Bikash, 2020. "Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data," Journal of Business Research, Elsevier, vol. 117(C), pages 443-449.
    7. Trkman, Peter & McCormack, Kevin, 2009. "Supply chain risk in turbulent environments--A conceptual model for managing supply chain network risk," International Journal of Production Economics, Elsevier, vol. 119(2), pages 247-258, June.
    8. 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.
    9. Hongchun Wang & Xinyan Zhang & Kebing Chen, 2022. "Research on Supply Chain Risk Transmission Mechanism Based on Improved SIRS Model," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, January.
    10. 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.
    11. Fu, Xiuwen & Xu, Xiaojie & Li, Wenfeng, 2024. "Cascading failure resilience analysis and recovery of automotive manufacturing supply chain networks considering enterprise roles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    12. Li, Zhuyue & Zhao, Peixin & Han, Xue, 2022. "Agri-food supply chain network disruption propagation and recovery based on cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    13. Lawrence V. Snyder & Zümbül Atan & Peng Peng & Ying Rong & Amanda J. Schmitt & Burcu Sinsoysal, 2016. "OR/MS models for supply chain disruptions: a review," IISE Transactions, Taylor & Francis Journals, vol. 48(2), pages 89-109, February.
    14. Dmitry Ivanov & Alexandre Dolgui, 2020. "Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 2904-2915, May.
    15. 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).
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    1. Fu, Xiuwen & Tian, Mengyao & Liu, Xiangwei & Wu, Shaomin & Peng, Rui, 2026. "Cascading failure resilience of dual-layer supply chain networks under enterprise competition with incomplete information," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    2. Liu, Kaiyuan & Xia, Yongxiang & Xia, Chengyi & Tu, Haicheng, 2025. "Impact of heterogeneity on risk propagation in supply chain networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 679(C).

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