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Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective

Author

Listed:
  • Bojana Bajic

    (Department of Industrial Engineering and Management, University of Novi Sad, 21000 Novi Sad, Serbia
    Institute for Artificial Intelligence Research and Developments of Serbia, 21000 Novi Sad, Serbia)

  • Nikola Suzic

    (Department of Industrial Engineering, University of Trento, 38123 Trento, Italy)

  • Slobodan Moraca

    (Department of Industrial Engineering and Management, University of Novi Sad, 21000 Novi Sad, Serbia)

  • Miladin Stefanović

    (Center for Quality, Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia)

  • Milos Jovicic

    (Institute for Artificial Intelligence Research and Developments of Serbia, 21000 Novi Sad, Serbia)

  • Aleksandar Rikalovic

    (Department of Industrial Engineering and Management, University of Novi Sad, 21000 Novi Sad, Serbia
    Institute for Artificial Intelligence Research and Developments of Serbia, 21000 Novi Sad, Serbia)

Abstract

In the last decade, researchers have focused on digital technologies within Industry 4.0. However, it seems the Industry 4.0 hype did not fulfil industry expectations due to many implementation challenges. Today, Industry 5.0 proposes a human-centric approach to implement digital sustainable technologies for smart quality improvement. One important aspect of digital sustainability is reducing the energy consumption of digital technologies. This can be achieved through a variety of means, such as optimizing energy efficiency, and data centres power consumption. Complementing and extending features of Industry 4.0, this research develops a conceptual model to promote Industry 5.0. The aim of the model is to optimize data without losing significant information contained in big data. The model is empowered by edge computing, as the Industry 5.0 enabler, which provides timely, meaningful insights into the system, and the achievement of real-time decision-making. In this way, we aim to optimize data storage and create conditions for further power and processing resource rationalization. Additionally, the proposed model contributes to Industry 5.0 from a social aspect by considering the knowledge, not only of experienced engineers, but also of workers who work on machines. Finally, the industrial application was done through a proof-of-concept using manufacturing data from the process industry, where the amount of data was reduced by 99.73% without losing significant information contained in big data.

Suggested Citation

  • Bojana Bajic & Nikola Suzic & Slobodan Moraca & Miladin Stefanović & Milos Jovicic & Aleksandar Rikalovic, 2023. "Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6032-:d:1112208
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    References listed on IDEAS

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    1. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    2. Ajay Kumar & Ravi Shankar & Alok Choudhary & Lakshman S. Thakur, 2016. "A big data MapReduce framework for fault diagnosis in cloud-based manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7060-7073, December.
    3. Junjie Li & Guohui Zhan & Xin Dai & Meng Qi & Bangfan Liu, 2022. "Innovation and Optimization Logic of Grassroots Digital Governance in China under Digital Empowerment and Digital Sustainability," Sustainability, MDPI, vol. 14(24), pages 1-28, December.
    4. Yair Wand & Ron Weber, 2002. "Research Commentary: Information Systems and Conceptual Modeling—A Research Agenda," Information Systems Research, INFORMS, vol. 13(4), pages 363-376, December.
    5. Fei Qiao & Juan Liu & Yumin Ma, 2021. "Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7139-7159, December.
    6. Iva Vuksanović Herceg & Vukašin Kuč & Veljko M. Mijušković & Tomislav Herceg, 2020. "Challenges and Driving Forces for Industry 4.0 Implementation," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
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    2. Abdullah Baz & Riaz Ahmed & Suhel Ahmad Khan & Sudesh Kumar, 2023. "Security Risk Assessment Framework for the Healthcare Industry 5.0," Sustainability, MDPI, vol. 15(23), pages 1-27, December.

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