AccScience Publishing / IJOSI / Volume 6 / Issue 3 / DOI: 10.6977/IJoSI.202103_6(3).0004
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Analysis and Application of Energy Management in Industry 4.0 with TRIZ Methodology

Mantle Yang Ming-Tien Tsai
Submitted: 8 April 2020 | Revised: 14 September 2020 | Accepted: 8 April 2020 | Published: 14 September 2020
© by the Authors. Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

The advent of Industry 4.0 takes our understanding of technology to a whole new level. The pursuit ofprofitability is gradually being replaced by business strategies that focus on comprehensive and sustainableoperations. As a consequence, the looming energy crisis has become the center of attention, making smart energymanagement solutions an indispensable cornerstone of industry transformation. For intelligent factories, in additionto upgrading manufacturing equipment, businesses can improve upon traditional models of energy management bycollecting and analyzing big data generated by the equipment. Smart energy management, in sum, is a system thateffectively coordinates, monitors, integrates, manages, and predicts the operation of multiple sets of equipment,creating a customized energy management platform for every business based on data analytics. The present study is acase study on the facility management system adopted by semiconductor manufacturers. The author discusses thedevelopmental trends in smart energy management within the context of Industry 4.0 based on “failure modes andeffects analysis (FMEA)” and the “theory of inventive problem solving (TRIZ).” Building on the results, the authorsummarizes the potential technologies that meet practical needs and the development of intelligent electricalcomponents that address potential failure modes. Finally, through the application of Internet of Things (IoT) and bigdata collection and transmission, businesses can conduct predictive maintenance on their in-service equipment toprevent system downtime, realizing the true benefits of intelligent management. The author hopes that the findings ofthis study can offer useful insights for relevant industries seeking to transform their businesses intelligently.

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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing