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Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers

Author

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  • Chih-Hung Hsu

    (Institute of Industrial Engineering, College of Transportation, FuJian University of Technology, Fuzhou 350118, China)

  • Xu He

    (Institute of Industrial Engineering, College of Transportation, FuJian University of Technology, Fuzhou 350118, China)

  • Ting-Yi Zhang

    (College of Management, FuJian University of Technology, Fuzhou 350118, China)

  • An-Yuan Chang

    (Institute of Industrial Management, College of Management, National Formosa University, Yunlin 632, Taiwan)

  • Wan-Ling Liu

    (Faculty of Economics and Business, University of Groningen, 9747 Groningen, The Netherlands)

  • Zhi-Qiang Lin

    (Institute of Industrial Engineering, College of Transportation, FuJian University of Technology, Fuzhou 350118, China)

Abstract

Given the increasing complexity of the global supply chain, it is an important issue to enhance the agilities of enterprises that manufacture new energy materials to reduce the ripple effects of supply chains. Quality function deployment (QFD) has been applied in many areas to solve multi-criteria decision making (MCDM) problems successfully. However, there is still lack of sufficient research on the use of MCDM to develop two house-of-quality systems in the supply chain of new energy materials manufacturing enterprises to determine ripple effect factors (REFs), supply chain agility indicators (SCAIs), and industry 4.0 enablers (I4Es). This study aimed to develop a valuable decision framework by integrating MCDM and QFD; using key I4Es to enhance the agility of supply chain and reduce or mitigate its ripple effects ultimately, this study provides an effective method for new energy materials manufacturers to develop supply chains that can rapidly respond to change and uncertainty. The case study considered China’s largest new energy materials manufacturing enterprise as the object and obtained important management insights, as well as practical significance, from implementing the proposed research framework. The study found the following to be the most urgent I4Es required to strengthen the agility of supply chain and reduce the key REFs: ensuring data privacy and security, guarding against legal risks, adopting digital transformation investment to improve economic efficiency, ramming IT infrastructure for big data management, and investing and using the new equipment of Industry 4.0. When these measures are improved, the agility of the supply chain can be improved, such as long-term cooperation with partners to strengthen trust relationships, supply chain information transparency and visualization to quickly respond to customer needs, and improving customer service levels and satisfaction. Finally, REFs, such as the bullwhip effect caused by inaccurate prediction, facility failure, and poor strain capacity caused by supply chain disruption, can be alleviated or eliminated. The proposed framework provides an effective strategy for formulating I4Es to strengthen supply chain agility (SCA) and mitigate ripple effects, as well as provides a reference for supply chain management of other manufacturing enterprises in the field of cleaner production.

Suggested Citation

  • Chih-Hung Hsu & Xu He & Ting-Yi Zhang & An-Yuan Chang & Wan-Ling Liu & Zhi-Qiang Lin, 2022. "Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1635-:d:813147
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    References listed on IDEAS

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    Cited by:

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    2. El-Awady Attia & Ali Alarjani & Md. Sharif Uddin & Ahmed Farouk Kineber, 2023. "Determining the Stationary Enablers of Resilient and Sustainable Supply Chains," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
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    4. Ze Wei & Hui Liu & Xuewen Tao & Kai Pan & Rui Huang & Wenjing Ji & Jianhai Wang, 2023. "Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis," Sustainability, MDPI, vol. 15(8), pages 1-29, April.

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