Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing
AbstractSmart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain. View Full-Text
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Moyne, J.; Iskandar, J. Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing. Processes 2017, 5, 39.
Moyne J, Iskandar J. Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing. Processes. 2017; 5(3):39.Chicago/Turabian Style
Moyne, James; Iskandar, Jimmy. 2017. "Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing." Processes 5, no. 3: 39.
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