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Ripple effect quantification by supplier risk exposure assessment

Author

Listed:
  • Aseem Kinra
  • Dmitry Ivanov
  • Ajay Das
  • Alexandre Dolgui

Abstract

Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis.

Suggested Citation

  • Aseem Kinra & Dmitry Ivanov & Ajay Das & Alexandre Dolgui, 2020. "Ripple effect quantification by supplier risk exposure assessment," International Journal of Production Research, Taylor & Francis Journals, vol. 58(18), pages 5559-5578, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:18:p:5559-5578
    DOI: 10.1080/00207543.2019.1675919
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    Citations

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    Cited by:

    1. Dmitry Ivanov & Alexandre Dolgui, 2022. "Stress testing supply chains and creating viable ecosystems," Operations Management Research, Springer, vol. 15(1), pages 475-486, June.
    2. 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).
    3. Mao Zheng & Ningning Cui & Yibin Zhang & Fangfang Zhang & Victor Shi, 2023. "Inventory Policies and Supply Chain Coordination under Logistics Route Disruption Risks," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    4. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry, 2023. "Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments," Omega, Elsevier, vol. 117(C).
    5. Catherine Marinagi & Panagiotis Reklitis & Panagiotis Trivellas & Damianos Sakas, 2023. "The Impact of Industry 4.0 Technologies on Key Performance Indicators for a Resilient Supply Chain 4.0," Sustainability, MDPI, vol. 15(6), pages 1-31, March.
    6. Can Ding & Li Liu & Yi Zheng & Jianxiu Liao & Wenxing Huang, 2022. "Role of Distribution Centers Disruptions in New Retail Supply Chain: An Analysis Experiment," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    7. Vimal K.E.K & Simon Peter Nadeem & Mahadharsan Ravichandran & Manavalan Ethirajan & Jayakrishna Kandasamy, 2022. "Resilience strategies to recover from the cascading ripple effect in a copper supply chain through project management," Operations Management Research, Springer, vol. 15(1), pages 440-460, June.
    8. 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.
    9. Tianyuan Zhang & Jiayao Li & Frederick Benaben, 2022. "A Simulation Framework Dedicated to Characterizing Risks and Cascading Effects in Collaborative Networks," Post-Print hal-03775883, HAL.
    10. Alam, Md Fahim Bin & Tushar, Saifur Rahman & Ahmed, Tazim & Karmaker, Chitra Lekha & Bari, A.B.M. Mainul & de Jesus Pacheco, Diego Augusto & Nayyar, Anand & Islam, Abu Reza Md Towfiqul, 2024. "Analysis of the enablers to deal with the ripple effect in food grain supply chains under disruption: Implications for food security and sustainability," International Journal of Production Economics, Elsevier, vol. 270(C).
    11. Hu, Hui & Xu, Jiajun & Liu, Mengqi & Lim, Ming K., 2023. "Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning," Journal of Business Research, Elsevier, vol. 156(C).
    12. Liu, Ming & Liu, Zhongzheng & Chu, Feng & Dolgui, Alexandre & Chu, Chengbin & Zheng, Feifeng, 2022. "An optimization approach for multi-echelon supply chain viability with disruption risk minimization," Omega, Elsevier, vol. 112(C).
    13. Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Jaya Priyadarshini & Rajesh Kr Singh & Ruchi Mishra & Surajit Bag, 2022. "Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach," Operations Management Research, Springer, vol. 15(1), pages 567-588, June.
    15. Abdolreza Roshani & Philip Walker-Davies & Glenn Parry, 2024. "Designing resilient supply chain networks: a systematic literature review of mitigation strategies," Annals of Operations Research, Springer, vol. 341(2), pages 1267-1332, October.
    16. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).

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