Big data analytics energy-saving strategies for air compressors in the semiconductor industry – an empirical study. Issue 6 (19th March 2022)

• Record Type: Journal Article
• Authors: Chang, Kuo-Hao; Sun, Yi-Jyun; Lai, Chi-An; Chen, Li-Der; Wang, Chih-Hung; Chen, Chung-Jung; Lin, Chih-Ming
• Is Part Of: International journal of production research. Volume 60:Issue 6(2022)
• Page Start: 1782
• Page End: 1794
• Publication Date: 2022-03-19
• Subjects: Big data analytics -- air compressors -- semiconductor industry -- dynamic programming -- energy efficiency; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2020.1870015
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Industry 4.0, smart manufacturing and its related technologies are now becoming the leading trend in the development of the manufacturing industry. One of the key drivers of Industry 4.0 is big data analytics, which can transform large amounts of data into useful information, enabling astute and rapid decision-making strategies when combined with expert domain knowledge. The semiconductor industry is the most important high-tech industry in Taiwan, but it is also one of the most energy-consuming industries in the country. Therefore, it is critical to improve the efficiency of the manufacturing process and reduce the overall energy consumption of facility systems. This research demonstrates how to apply big data analytics in the semiconductor industry to explore the relationships of various machine parameters, develop predictive models for machine energy efficiency and apply optimisation tools to minimise energy consumption, while meeting the production demands. An empirical study is conducted in conjunction with a semiconductor corporation in Taiwan, targeting the air compressor system in its factory. The research framework is shown to be capable of assisting semiconductor fabrication plant decision-makers in optimising machine configurations, resulting in more than 10% savings on energy consumption and significantly decreased manufacturing costs.