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A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing
Affiliation:1. Department of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan;2. Department of Business Administration, Chaoyang University of Technology, Taichung, Taiwan;4. Department of Financial Management, Nation Defense University, Taiwan;1. School of Engineering Technology, Purdue University, West Lafayette, USA;2. Department of Mechanical Engineering, University of Bristol, UK;3. School of Mechanical Engineering, Zhejiang University, Hangzhou, China;4. School of Environmental, Civil, Agricultural, and Mechanical Engineering, University of Georgia, Athens, GA, USA;5. Department of Mechanical Engineering, University of Michigan, Ann Arbor, USA
Abstract:Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.
Keywords:Sustainable supplier evaluation  Data-driven  Industry 5.0  VC-DRSA  MCDM
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