Authors: James Moyne and Jimmy Iskandar

 

Journal Title: Processes

 

ISSN: 2227-9717 (Online)

 
Publisher: MDPI AG
 
Abstract
 
Smart 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.
 
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
 

Illustration Photo: Sandia microelectronics technician Patty Chenevey studies a wafer, a thin slice of semiconductor material used in the fabrication of microdevices. (credits: Randy Montoya / Sandia Labs / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

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