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