Deploying Industry 5.0 drivers to enhance sustainable supply chain risk resilience

ABSTRACT

With global supply chains facing increasing complexity and uncertainty, mitigating sustainable supply chain risks (SSCRs) has become an important research topic. Industry 5.0 (I5.0) is a supplement to, and re-development of, Industry 4.0, but no research on improving supply chain resilience (SCRE) through I5.0 drivers to mitigate SSCRs currently exists. Therefore, we developed a QFD approach to investigate the relationship between SSCRs, SCRE, and I5.0 drivers. This study utilises the FDM-FDISM-ANP-TOPSIS method, considers China’s largest private logistics enterprises as the object of empirical analysis, and establishes a sustainable supply chain identification system and SCRE evaluation index system for logistics enterprises through two-stage House of Quality. The findings reveal that 20 key I5.0 significantly bolster 17 SCRE, thereby mitigating 13 SSCRs by QFD-MCDM framework. These I5.0 drivers play a pivotal role in enhancing SCRE of logistics enterprises. This study contributes to the field of supply chain risk resilience in sustainable engineering by integrating the perspectives of SSCRs, SCRE and I5.0. It underscores the necessity for managers in logistics enterprises to develop SCRE solutions that incorporate both SSCRs and I5.0 considerations. Furthermore, our findings offer valuable insights for other industries aiming to bolster their supply chain competitive advantages in an increasingly complex global landscape.

KEYWORDS:

• Industry 5.0 driver
• sustainable supply chain risk
• supply chain resilience
• quality function deployment

Acknowledgments

The authors of this paper would like to acknowledge greatly to all the authors who have published valuable research articles and books that have been used for this review paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article.

Authors contributions

C.-H.H.: Conceptualization, Methodology, Writing-review and editing

J.Z.W: Methodology, Software, Funding acquisition, Formal analysis, Writing-original draft

T.Y.Z: Data curation, Visualization, Supervision

J.Y.C: Visualization, Project administration, Resources

All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

This paper was supported by the Special Fund of Education and Scientific Research of Fujian Provincial Finance Department [Grant No. GY-Z21001] and Surface of the State Natural Science Fund projects (61976055).

Notes on contributors

Chih-Hung Hsu

Chih-Hung Hsu received his bachelor’s degree in Industrial Management from Taiwan University of Science and Technology in 1996, master’s degree in Industrial Management from Taiwan University of Science and Technology in 1999, doctor’s degree in industrial Engineering and Engineering Management from Tsinghua University in Hsinchu, Taiwan in 2005, and master’s degree in International logistics from the Department of Shipping Management, Ocean University of Taiwan in 2011. His current research involves multi-attribute decision making and data mining in resilient, agile, green and sustainable supply chains.

Jin-Zheng Wu

Jin-Zheng Wu received the B.Sc. degree in Transportation from the Fujian University of Technology, Fujian, China, in 2020. He is currently pursuing the M.Sc. degree in Mechanical & Industrial Engineering from the Fujian University of Technology, Fujian, China. His main areas of interest are multi-attribute decision making, supply chains and Industry5.0.

Ting-Yi Zhang

Ting-Yi Zhang served as a associate professor of Institute of Industrial Engineering, College of Management, FuJian University of Technology. His main areas of interest are supply chain management, production system scheduling and optimization.

Jia-Yi Chen

Jia-Yi Chen received the B.Sc. degree in Logistics management from the Fujian University of Technology, Fujian, China, in 2022.