A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

被引:32
作者
Lo, Huai-Wei [1 ]
机构
[1] Natl Yunlin Univ Sci & Technol, Dept Ind Engn & Management, Touliu, Taiwan
关键词
Sustainable supplier evaluation; Data-driven; Industry; 5; 0; VC-DRSA; MCDM; TOPSIS;
D O I
10.1016/j.aei.2023.101998
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 competi-tiveness 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 demon-strate 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.
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页数:12
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共 48 条
  • [1] Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development
    Bag, Surajit
    Gupta, Shivam
    Kumar, Sameer
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2021, 231
  • [2] Industry 4.0 technologies assessment: A sustainability perspective
    Bai, Chunguang
    Dallasega, Patrick
    Orzes, Guido
    Sarkis, Joseph
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2020, 229
  • [3] Supplier selection using extended IT2 fuzzy TOPSIS and IT2 fuzzy MOORA considering subjective and objective factors
    Bera, Ashoke Kumar
    Jana, Dipak Kumar
    Banerjee, Debamalya
    Nandy, Titas
    [J]. SOFT COMPUTING, 2020, 24 (12) : 8899 - 8915
  • [4] A hybrid decision-making model for sustainable supplier evaluation in electronics manufacturing
    Chang, Tai-Wu
    Pai, Chun-Jui
    Lo, Huai-Wei
    Hu, Shu-Kung
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2021, 156
  • [5] Investigating interdependencies of sustainable supplier selection criteria: an appraisal using ISM
    Chauhan, Avanish Singh
    Badhotiya, Gaurav Kumar
    Soni, Gunjan
    Kumari, Prem
    [J]. JOURNAL OF GLOBAL OPERATIONS AND STRATEGIC SOURCING, 2020, 13 (02) : 195 - 210
  • [6] Rethinking companies' culture through knowledge management lens during Industry 5.0 transition
    Cillo, Valentina
    Gregori, Gian Luca
    Daniele, Lucia Michela
    Caputo, Francesco
    Bitbol-Saba, Nathalie
    [J]. JOURNAL OF KNOWLEDGE MANAGEMENT, 2022, 26 (10) : 2485 - 2498
  • [7] An integrated framework for sustainable supplier development through supplier evaluation based on sustainability indicators
    Coskun, Serdar Semih
    Kumru, Mesut
    Kan, Natali Maya
    [J]. JOURNAL OF CLEANER PRODUCTION, 2022, 335
  • [8] Interpretable data science for decision making
    Coussement, Kristof
    Benoit, Dries F.
    [J]. DECISION SUPPORT SYSTEMS, 2021, 150
  • [9] Modelling flexible decisions about sustainable supplier selection in multitier sustainable supply chain management
    Cui, Li
    Wu, Hao
    Dai, Jing
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2023, 61 (14) : 4603 - 4624
  • [10] Identifying areas vulnerable to homicide using multiple criteria analysis and spatial analysis
    de Miranda Mota, Caroline Maria
    Jardim de Figueiredo, Ciro Jose
    Viana e Sousa Pereira, Debora
    [J]. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2021, 100