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

Author

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  • 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
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    References listed on IDEAS

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