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Comparing industry 4.0 maturity models in the perspective of TQM principles using Fuzzy MCDM methods

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  • Elibal, Kerem
  • Özceylan, Eren

Abstract

The purpose of this paper is to conduct a comparison for Industry 4.0 maturity models in the context of Total Quality Management (TQM) principles. TQM principles have been used as criteria for evaluating alternative Industry 4.0 maturity models. Because of the critics about TQM concept for having an inadequate theoretical foundation and highly expert insight dependent nature, fuzzy logic principles have been used. In this study, linguistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used for ranking and Decision Making Trial and Evaluation Laboratory (DEMATEL) method has been used for criteria weighting to determine which maturity model mostly fits TQM principles. Four Industry 4.0 maturity models have been evaluated according to seven criteria and 33 sub-criteria. According to the results, the model of Schumacher et al. (2016) has the highest score among the compared models. Another result is among the seven main criteria customer focus has the most influence and relationship management has the less, and among 33 sub-criteria risk identification has the most influence and relationship management has the less. Findings represented in this study can serve academicians and practitioners to compare, develop and/or improve Industry 4.0 maturity models.

Suggested Citation

  • Elibal, Kerem & Özceylan, Eren, 2022. "Comparing industry 4.0 maturity models in the perspective of TQM principles using Fuzzy MCDM methods," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521008106
    DOI: 10.1016/j.techfore.2021.121379
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    2. Krishna Kumar Dadsena & Pushpesh Pant & Sanjoy Kumar Paul & Saurabh Pratap, 2024. "Overcoming strategies for supply chain digitization barriers: Implications for sustainable development goals," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 3887-3910, July.
    3. Abdulaziz Attaallah & Khalil al-Sulbi & Areej Alasiry & Mehrez Marzougui & Syed Anas Ansar & Alka Agrawal & Md Tarique Jamal Ansari & Raees Ahmad Khan, 2023. "Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    4. Palash Saha & Subrata Talapatra & H. M. Belal & Victoria Jackson, 2022. "Unleashing the Potential of the TQM and Industry 4.0 to Achieve Sustainability Performance in the Context of a Developing Country," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(4), pages 495-513, December.
    5. Canbay, Kamile & Akman, Gulsen, 2023. "Investigating changes of total quality management principles in the context of Industry 4.0: Viewpoint from an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    6. Tsai, Pei-Hsuan, 2024. "Exploring consumer purchase intention in cross-boundary retail alliances with gamified OMO virtual-physical fusion services," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).

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