Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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
ORCID logo ,
William D. Chicaiza
,
Michael Ahern
,
Juan Manuel Escaño
ORCID logo ,
Andriy Hryshchenko
ORCID logo ,
Olan Casey
,
Aidan Cloonan
,
Dominic T.J. O'Sullivan
ORCID logo ,
Ken Bruton
ORCID logo ,
Alan McGibney
ORCID logo
Version 1 : Received: 28 February 2024 / Approved: 28 February 2024 / Online: 28 February 2024 (13:39:05 CET)

A peer-reviewed article of this Preprint also exists.

Ranade, A.; Gómez, J.; de Juan, A.; Chicaiza, W.D.; Ahern, M.; Escaño, J.M.; Hryshchenko, A.; Casey, O.; Cloonan, A.; O’Sullivan, D.; Bruton, K.; McGibney, A. Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications. Energies 2024, 17, 1818. Ranade, A.; Gómez, J.; de Juan, A.; Chicaiza, W.D.; Ahern, M.; Escaño, J.M.; Hryshchenko, A.; Casey, O.; Cloonan, A.; O’Sullivan, D.; Bruton, K.; McGibney, A. Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications. Energies 2024, 17, 1818.

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 their 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.

Keywords

Industry 4.0; Energy Efficiency; Sustainability; FDD; Renewable Integration; Smart Manufacturing

Subject

Engineering, Industrial and Manufacturing Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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