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The quality management ecosystem for predictive maintenance in the Industry 4.0 era

Author

Listed:
  • Sang M. Lee

    (University of Nebraska-Lincoln)

  • DonHee Lee

    (Inha University)

  • Youn Sung Kim

    (Inha University)

Abstract

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five real-world cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction. Such predictive quality management systems can become living ecosystems that can perform cause-effect analysis, big data monitoring and analytics, and effective decision-making in real time. This study proposes several practical implications for actual design and implementation of effective predictive quality management systems in the Industry 4.0 era. However, the living predictive quality management ecosystem should be the product of the organizational culture that nurtures collaborative efforts of all stakeholders, sharing of information, and co-creation of shared goals.

Suggested Citation

  • Sang M. Lee & DonHee Lee & Youn Sung Kim, 2019. "The quality management ecosystem for predictive maintenance in the Industry 4.0 era," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-11, December.
    • Handle: RePEc:spr:ijoqin:v:5:y:2019:i:1:d:10.1186_s40887-019-0029-5
    DOI: 10.1186/s40887-019-0029-5
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    Citations

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    Cited by:

    1. Neeraj Yadav & Ravi Shankar & Surya Prakash Singh, 2021. "Hierarchy of Critical Success Factors (CSF) for Lean Six Sigma (LSS) in Quality 4.0," International Journal of Global Business and Competitiveness, Springer, vol. 16(1), pages 1-14, June.
    2. Justyna Zywiolek & Michal Molenda & Joanna Rosak-Szyrocka, 2021. "Satisfaction with the Implementation of Industry 4.0 Among Manufacturing Companies in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 469-479.
    3. Justyna Zywiolek & Michal Molenda & Joanna Rosak-Szyrocka, 2021. "Satisfaction with the Implementation of Industry 4.0 Among Manufacturing Companies in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 592-603.
    4. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    5. Sang M. Lee & DonHee Lee, 2020. "“Untact”: a new customer service strategy in the digital age," Service Business, Springer;Pan-Pacific Business Association, vol. 14(1), pages 1-22, March.
    6. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.

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