IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13125-d941106.html
   My bibliography  Save this article

Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model

Author

Listed:
  • Fangzhong Qi

    (School of Management, Zhejiang University of Technology, Hangzhou 310023, China)

  • Leilei Zhang

    (School of Management, Zhejiang University of Technology, Hangzhou 310023, China)

  • Kexiang Zhuo

    (School of Management, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xiuyan Ma

    (School of Management, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

In a dynamic, uncertain environment, increased supply chain resilience can improve business quality. Predicting changes in enterprise supply chain resilience can help enterprises adjust their operational strategy timeously and reduce the risk of supply and demand interruption. First, a comprehensive resilience assessment framework for manufacturing enterprises was constructed from the perspective of the supply chain, and an improved technique for order of preference by similarity to the ideal solution (TOPSIS) method was used to quantify the resilience level. Considering that the resilience index is easily affected by uncertain factors, and this produces large fluctuations, the buffer operator and metabolism idea are introduced to improve the grey prediction model. This improvement can realize dynamic tracking of the enterprise resilience index and evaluate changes in the enterprise resilience level. Finally, through the analysis of the supply chain data of a famous electronic manufacturing enterprise in China over a two-and-a-half-year period, the results show that the improved TOPSIS method and the improved grey prediction model are effective in improving the supply chain resilience of manufacturing enterprises. This study provides a reference method for manufacturing enterprises to improve their supply chain resilience.

Suggested Citation

  • Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13125-:d:941106
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13125/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13125/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jingjing Pei & Wen Liu, 2019. "Evaluation of Chinese Enterprise Safety Production Resilience Based on a Combined Gray Relevancy and BP Neural Network Model," Sustainability, MDPI, vol. 11(16), pages 1-13, August.
    2. Naghshineh, Bardia & Carvalho, Helena, 2022. "The implications of additive manufacturing technology adoption for supply chain resilience: A systematic search and review," International Journal of Production Economics, Elsevier, vol. 247(C).
    3. Jun Zhang & Tongyuan Wang & Jianpeng Chang & Yan Gou & Wen Yi, 2021. "Forecasting the Number of the Wounded after an Earthquake Disaster Based on the Continuous Interval Grey Discrete Verhulst Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-11, April.
    4. Dongxiao Niu & Gengqi Wu & Zhengsen Ji & Dongyu Wang & Yuying Li & Tian Gao, 2021. "Evaluation of Provincial Carbon Neutrality Capacity of China Based on Combined Weight and Improved TOPSIS Model," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    5. Xin Yu & Sid Suntrayuth & Jiafu Su, 2020. "A Comprehensive Evaluation Method for Industrial Sewage Treatment Projects Based on the Improved Entropy-TOPSIS," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    6. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    7. Zixin Dou & BeiBei Wu & Yanming Sun & Tao Wang, 2021. "The Competitiveness of Manufacturing and Its Driving Factors: A Case Study of G20 Participating Countries," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    8. Rajesh, R. & Agariya, Arun Kumar & Rajendran, Chandrasekharan, 2021. "Predicting resilience in retailing using grey theory and moving probability based Markov models," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    9. Qiang Xiao & Hongshuang Wang, 2022. "Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(11), pages 1-14, June.
    10. Dmitry Ivanov & Alexandre Dolgui, 2019. "Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 5119-5136, August.
    11. Iman Kazemian & S. Ali Torabi & Christopher W. Zobel & Yuhong Li & Milad Baghersad, 2022. "A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators," Operational Research, Springer, vol. 22(3), pages 1853-1883, July.
    12. Seyedmohsen Hosseini & Abdullah Al Khaled, 2019. "A hybrid ensemble and AHP approach for resilient supplier selection," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 207-228, January.
    13. 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.
    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. Roya Ghamari & Mohammad Mahdavi-Mazdeh & Seyed Farid Ghannadpour, 2022. "Resilient and sustainable supplier selection via a new framework: a case study from the steel industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 10403-10441, August.
    2. 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.
    3. 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.
    4. Iman Kazemian & S. Ali Torabi & Christopher W. Zobel & Yuhong Li & Milad Baghersad, 2022. "A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators," Operational Research, Springer, vol. 22(3), pages 1853-1883, July.
    5. 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.
    6. Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
    7. Madjid Tavana & Salman Nazari-Shirkouhi & Hamidreza Farzaneh Kholghabad, 2021. "An integrated quality and resilience engineering framework in healthcare with Z-number data envelopment analysis," Health Care Management Science, Springer, vol. 24(4), pages 768-785, December.
    8. Balezentis, Tomas & Zickiene, Agne & Volkov, Artiom & Streimikiene, Dalia & Morkunas, Mangirdas & Dabkiene, Vida & Ribasauskiene, Erika, 2023. "Measures for the viable agri-food supply chains: A multi-criteria approach," Journal of Business Research, Elsevier, vol. 155(PA).
    9. Kaur, Harpreet & Prakash Singh, Surya, 2021. "Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies," International Journal of Production Economics, Elsevier, vol. 231(C).
    10. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    11. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    12. Jingsi Zhang & Liangqun Qi, 2021. "Crisis Preparedness of Healthcare Manufacturing Firms during the COVID-19 Outbreak: Digitalization and Servitization," IJERPH, MDPI, vol. 18(10), pages 1-23, May.
    13. Zixin Dou & Yanming Sun & Tao Wang & Huiyin Wan & Shiqi Fan, 2021. "Exploring Regional Advanced Manufacturing and Its Driving Factors: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area," IJERPH, MDPI, vol. 18(11), pages 1-14, May.
    14. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    15. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    16. 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.
    17. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    18. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    19. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    20. Amin Mahmoudi & Saad Ahmed Javed, 2022. "Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach," Group Decision and Negotiation, Springer, vol. 31(5), pages 1051-1096, October.

    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:gam:jsusta:v:14:y:2022:i:20:p:13125-:d:941106. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.