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Predictive Analytics for Equipment Maintenance Operations: A Case Study From the Semiconductor Industry

Predictive Analytics for Equipment Maintenance Operations: A Case Study From the Semiconductor Industry

Simon J. Preis
Copyright: © 2022 |Pages: 19
ISBN13: 9781799877127|ISBN10: 1799877124|ISBN13 Softcover: 9781799877134|EISBN13: 9781799877141
DOI: 10.4018/978-1-7998-7712-7.ch018
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MLA

Preis, Simon J. "Predictive Analytics for Equipment Maintenance Operations: A Case Study From the Semiconductor Industry." Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies, edited by Martin George Wynn, IGI Global, 2022, pp. 340-358. https://doi.org/10.4018/978-1-7998-7712-7.ch018

APA

Preis, S. J. (2022). Predictive Analytics for Equipment Maintenance Operations: A Case Study From the Semiconductor Industry. In M. Wynn (Ed.), Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies (pp. 340-358). IGI Global. https://doi.org/10.4018/978-1-7998-7712-7.ch018

Chicago

Preis, Simon J. "Predictive Analytics for Equipment Maintenance Operations: A Case Study From the Semiconductor Industry." In Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies, edited by Martin George Wynn, 340-358. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-7712-7.ch018

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Abstract

Predictive maintenance (PdM) is a key application of data analytics in semiconductor manufacturing. The optimization of equipment performance has been found to deliver significant revenue benefits, especially in the wafer fabrication process. This chapter addresses two main research objectives: first, to investigate the particular challenges and opportunities of implementing PdM for wafer fabrication equipment and, second, to identify the implications of PdM on key performance indicators in the wafer fabrication process. The research methodology is based on a detailed case study of a wafer fabrication facility and expert interviews. The findings indicate the potential benefits of PdM beyond improving equipment maintenance operations, and the chapter concludes that the quality of analytics models for PdM in wafer fabrication is critical, but this depends on challenging data preparation processes, per machine type. Without valid predictions, decision-making ability and benefits delivery will be limited.

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