IDEAS home Printed from https://ideas.repec.org/a/spr/ijoqin/v5y2019i1d10.1186_s40887-019-0029-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40887-019-0029-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1186/s40887-019-0029-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijoqin:v:5:y:2019:i:1:d:10.1186_s40887-019-0029-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.