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Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Author

Listed:
  • Akshay Ranade

    (Civil and Environmental Engineering & SFI MaREI Centre for Energy, Climate and Marine, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland)

  • Javier Gómez

    (Department of Systems Engineering and Automatic Control, Universidad de Sevilla, 41004 Sevilla, Spain)

  • Andrew de Juan

    (Nimbus Research Centre, Munster Technological University, T12 P928 Cork, Ireland)

  • William D. Chicaiza

    (Department of Systems Engineering and Automatic Control, Universidad de Sevilla, 41004 Sevilla, Spain)

  • Michael Ahern

    (Civil and Environmental Engineering & SFI MaREI Centre for Energy, Climate and Marine, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland)

  • Juan M. Escaño

    (Department of Systems Engineering and Automatic Control, Universidad de Sevilla, 41004 Sevilla, Spain)

  • Andriy Hryshchenko

    (Civil and Environmental Engineering & SFI MaREI Centre for Energy, Climate and Marine, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland)

  • Olan Casey

    (DePuy Synthes, Ringaskiddy, P43 ED82 Cork, Ireland)

  • Aidan Cloonan

    (DePuy Synthes, Ringaskiddy, P43 ED82 Cork, Ireland)

  • Dominic O’Sullivan

    (Civil and Environmental Engineering & SFI MaREI Centre for Energy, Climate and Marine, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland)

  • Ken Bruton

    (Civil and Environmental Engineering & SFI MaREI Centre for Energy, Climate and Marine, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland)

  • Alan McGibney

    (Nimbus Research Centre, Munster Technological University, T12 P928 Cork, Ireland)

Abstract

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

Suggested Citation

  • Akshay Ranade & Javier Gómez & Andrew de Juan & William D. Chicaiza & Michael Ahern & Juan M. Escaño & Andriy Hryshchenko & Olan Casey & Aidan Cloonan & Dominic O’Sullivan & Ken Bruton & Alan McGibney, 2024. "Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications," Energies, MDPI, vol. 17(8), pages 1-28, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1818-:d:1373263
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    References listed on IDEAS

    as
    1. Sven-Vegard Buer & Marco Semini & Jan Ola Strandhagen & Fabio Sgarbossa, 2021. "The complementary effect of lean manufacturing and digitalisation on operational performance," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 1976-1992, April.
    2. Machado, Diogo Ortiz & Chicaiza, William D. & Escaño, Juan M. & Gallego, Antonio J. & de Andrade, Gustavo A. & Normey-Rico, Julio E. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Digital twin of a Fresnel solar collector for solar cooling," Applied Energy, Elsevier, vol. 339(C).
    3. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    4. Gómez, Javier & Chicaiza, William D. & Escaño, Juan M. & Bordons, Carlos, 2023. "A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms," Renewable Energy, Elsevier, vol. 215(C).
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