IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6032-d1112208.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6032/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/6032/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. 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.
    2. Paolo Morganti & Rosa Carolina Valdes, 2023. "The Perils of Asymmetrical Technological Changes in a Knowledge Economy with Complete Markets," Sustainability, MDPI, vol. 15(17), pages 1-17, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    2. Bo-Rui Yan & Qian-Li Dong & Qian Li & Fahim UI Amin & Jia-Ni Wu, 2021. "A Study on the Coupling and Coordination between Logistics Industry and Economy in the Background of High-Quality Development," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
    3. E. Skordilis & R. Moghaddass, 2017. "A condition monitoring approach for real-time monitoring of degrading systems using Kalman filter and logistic regression," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5579-5596, October.
    4. Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
    5. Ajay Kumar & Ram D. Gopal & Ravi Shankar & Kim Hua Tan, 2022. "Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering," Post-Print hal-03630420, HAL.
    6. Tawse, Alex & Tabesh, Pooya, 2023. "Thirty years with the balanced scorecard: What we have learned," Business Horizons, Elsevier, vol. 66(1), pages 123-132.
    7. Artur Pollak & Agata Hilarowicz & Maciej Walczak & Damian Gąsiorek, 2020. "A Framework of Action for Implementation of Industry 4.0. an Empirically Based Research," Sustainability, MDPI, vol. 12(14), pages 1-16, July.
    8. Heise, David & Strecker, Stefan & Frank, Ulrich, 2014. "ControlML: A domain-specific modeling language in support of assessing internal controls and the internal control system," International Journal of Accounting Information Systems, Elsevier, vol. 15(3), pages 224-245.
    9. Roger Clarke, 2022. "Research opportunities in the regulatory aspects of electronic markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 179-200, March.
    10. Theerasak Nitlarp & Supaporn Kiattisin, 2022. "The Impact Factors of Industry 4.0 on ESG in the Energy Sector," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    11. Titov Sergei & Trachuk Arkady & Linder Natalya & RD Pathak & Danny Samson & Zafar Husain & S Sushil, 2023. "Digital transformation enablers in high-tech and low-tech companies: A comparative analysis," Australian Journal of Management, Australian School of Business, vol. 48(4), pages 801-843, November.
    12. Roman Lukyanenko & Andrea Wiggins & Holly K. Rosser, 0. "Citizen Science: An Information Quality Research Frontier," Information Systems Frontiers, Springer, vol. 0, pages 1-23.
    13. Görkem Sariyer & Mustafa Gokalp Ataman & Sachin Kumar Mangla & Yigit Kazancoglu & Manoj Dora, 2023. "Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations," Annals of Operations Research, Springer, vol. 328(1), pages 1073-1103, September.
    14. Peter Fettke, 2009. "Ansätze der Informationsmodellierung und ihre betriebswirtschaftliche Bedeutung: Eine Untersuchung der Modellierungspraxis in Deutschland," Schmalenbach Journal of Business Research, Springer, vol. 61(5), pages 550-580, August.
    15. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    16. Albérico Travassos Rosário & Joana Carmo Dias, 2023. "The New Digital Economy and Sustainability: Challenges and Opportunities," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    17. Parmar, Rashik & Leiponen, Aija & Thomas, Llewellyn D.W., 2020. "Building an organizational digital twin," Business Horizons, Elsevier, vol. 63(6), pages 725-736.
    18. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    19. Syed Hammad Mian & Bashir Salah & Wadea Ameen & Khaja Moiduddin & Hisham Alkhalefah, 2020. "Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities," Sustainability, MDPI, vol. 12(15), pages 1-33, July.
    20. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).

    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:gam:jsusta:v:15:y:2023:i:7:p:6032-:d:1112208. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.