IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v589y2022ics0378437121008712.html
   My bibliography  Save this article

Agri-food supply chain network disruption propagation and recovery based on cascading failure

Author

Listed:
  • Li, Zhuyue
  • Zhao, Peixin
  • Han, Xue

Abstract

In recent years, some extreme weather and public health events have led to frequent disruptions in agri-food supply chain networks in China, and scholars recognize the importance of this problem, but there are relatively few studies on the disruption propagation. Firstly, we construct the agri-food supply chain networks and the weak tie networks, and innovatively introduce weak ties into the disruption propagation of the agri-food supply chain networks. Secondly, we analyze the impact of two strategies, strengthening existing business relationships and establishing new business relationships, on the disruption propagation of agri-food supply chain networks under extreme natural disasters or unexpected wholesale market closures. Based on the results, the impact of disruption recovery on supply–demand relationships in agri-food supply chain network is analyzed. This research provides decision-making reference against supply chain disruptions and related problems.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:589:y:2022:i:c:s0378437121008712
    DOI: 10.1016/j.physa.2021.126611
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121008712
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2021.126611?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.

    References listed on IDEAS

    as
    1. Erfan Babaee Tirkolaee & Zahra Dashtian & Gerhard-Wilhelm Weber & Hana Tomaskova & Mehdi Soltani & Nasim Sadat Mousavi, 2021. "An Integrated Decision-Making Approach for Green Supplier Selection in an Agri-Food Supply Chain: Threshold of Robustness Worthiness," Mathematics, MDPI, vol. 9(11), pages 1-30, June.
    2. Youyu Chen & Tong Shu & Shou Chen & Shouyang Wang & Kin Keung Lai & Lu Gan, 2017. "Strong–weak collaborative management in coping supply chain disruption risk transmission based on scale-free networks," Applied Economics, Taylor & Francis Journals, vol. 49(39), pages 3943-3958, August.
    3. Emel Savku & Gerhard-Wilhelm Weber, 2018. "A Stochastic Maximum Principle for a Markov Regime-Switching Jump-Diffusion Model with Delay and an Application to Finance," Journal of Optimization Theory and Applications, Springer, vol. 179(2), pages 696-721, November.
    4. Juan Yang & Haorui Liu, 2018. "Research of Vulnerability for Fresh Agricultural-Food Supply Chain Based on Bayesian Network," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-17, December.
    5. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    6. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    7. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.
    8. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Complex interdependent supply chain networks: Cascading failure and robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 58-69.
    9. Armin Jabbarzadeh & Behnam Fahimnia & Fatemeh Sabouhi, 2018. "Resilient and sustainable supply chain design: sustainability analysis under disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5945-5968, September.
    10. Duan, Dong-Li & Ling, Xiao-Dong & Wu, Xiao-Yue & OuYang, Di-Hua & Zhong, Bin, 2014. "Critical thresholds for scale-free networks against cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 252-258.
    11. 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.
    12. 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.
    13. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    14. Ledwoch, Anna & Yasarcan, Hakan & Brintrup, Alexandra, 2018. "The moderating impact of supply network topology on the effectiveness of risk management," International Journal of Production Economics, Elsevier, vol. 197(C), pages 13-26.
    15. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    16. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.
    17. Busra Zeynep Temocin & Ralf Korn & A. Sevtap Selcuk-Kestel, 2018. "Constant proportion portfolio insurance in defined contribution pension plan management under discrete-time trading," Annals of Operations Research, Springer, vol. 260(1), pages 515-544, January.
    18. Catherine Brinkley, 2018. "The Small World of the Alternative Food Network," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    19. Busra Zeynep Temocin & Ralf Korn & A. Sevtap Selcuk-Kestel, 2018. "Constant proportion portfolio insurance in defined contribution pension plan management," Annals of Operations Research, Springer, vol. 266(1), pages 329-348, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    2. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications," International Journal of Production Economics, Elsevier, vol. 232(C).
    3. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    4. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    5. Lydia Novoszel & Tina Wakolbinger, 2022. "Meta-analysis of Supply Chain Disruption Research," SN Operations Research Forum, Springer, vol. 3(1), pages 1-25, March.
    6. 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).
    7. Manupati, V.K. & Schoenherr, Tobias & Ramkumar, M. & Panigrahi, Suraj & Sharma, Yash & Mishra, Prakriti, 2022. "Recovery strategies for a disrupted supply chain network: Leveraging blockchain technology in pre- and post-disruption scenarios," International Journal of Production Economics, Elsevier, vol. 245(C).
    8. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
    9. Niels Bugert & Rainer Lasch, 2023. "Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks," Journal of Business Economics, Springer, vol. 93(5), pages 859-889, July.
    10. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    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. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
    13. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    14. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    15. Laura M. Canevari‐Luzardo & Frans Berkhout & Mark Pelling, 2020. "A relational view of climate adaptation in the private sector: How do value chain interactions shape business perceptions of climate risk and adaptive behaviours?," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 432-444, February.
    16. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    17. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    18. An Chen & Thai Nguyen & Manuel Rach, 2021. "A collective investment problem in a stochastic volatility environment: The impact of sharing rules," Annals of Operations Research, Springer, vol. 302(1), pages 85-109, July.
    19. 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.
    20. Magnani, Marco, 2024. "An analysis of precautionary behavior in retirement decision making with an application to pension system reform," Insurance: Mathematics and Economics, Elsevier, vol. 117(C), pages 99-113.

    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:eee:phsmap:v:589:y:2022:i:c:s0378437121008712. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.