IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i6p1782-1794.html
   My bibliography  Save this article

Big data analytics energy-saving strategies for air compressors in the semiconductor industry – an empirical study

Author

Listed:
  • Kuo-Hao Chang
  • Yi-Jyun Sun
  • Chi-An Lai
  • Li-Der Chen
  • Chih-Hung Wang
  • Chung-Jung Chen
  • Chih-Ming Lin

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.

Suggested Citation

  • Kuo-Hao Chang & Yi-Jyun Sun & Chi-An Lai & Li-Der Chen & Chih-Hung Wang & Chung-Jung Chen & Chih-Ming Lin, 2022. "Big data analytics energy-saving strategies for air compressors in the semiconductor industry – an empirical study," International Journal of Production Research, Taylor & Francis Journals, vol. 60(6), pages 1782-1794, March.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:6:p:1782-1794
    DOI: 10.1080/00207543.2020.1870015
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2020.1870015
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2020.1870015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song, Yanwu & Dong, Ying, 2024. "Influence of resource compensation and complete information on green sustainability of semiconductor supply chains," International Journal of Production Economics, Elsevier, vol. 271(C).
    2. Zhao, Wenxuan & Li, Hangxin & Wang, Shengwei, 2024. "A generic design optimization framework for semiconductor cleanroom air-conditioning systems integrating heat recovery and free cooling for enhanced energy performance," Energy, Elsevier, vol. 286(C).
    3. Qiu, Hailing & Tseng, Shuan Wei & Zhang, Xuan & Huang, Caiyan & Wu, Kuo-Jui, 2024. "Revealing the compound interrelationships toward sustainable transition in semiconductor supply chain: A sensitivity analysis," International Journal of Production Economics, Elsevier, vol. 271(C).
    4. Zheng, Deyuan & Song, Hang & Zhao, Chunguang & Liu, Yujiao & Zhao, Wenhao, 2024. "Is it possible for semiconductor companies to reduce carbon emissions through digital transformation? Evidence from China," International Journal of Production Economics, Elsevier, vol. 272(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:60:y:2022:i:6:p:1782-1794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.