Journal Published Online: 11 June 2020
Volume 4, Issue 2

Foundations of Information Governance for Smart Manufacturing

CODEN: SSMSCY

Abstract

The manufacturing systems of the future will be even more dependent on data than they are today. More and more data and information are being collected and communicated throughout product development life cycles and across manufacturing value chains. To enable smarter manufacturing operations, new equipment often includes built-in data collection capabilities. Older equipment can be retrofitted inexpensively with sensors to collect a wide variety of data. Many manufacturers are in a quandary as to what to do with increasing quantities of data. Much hype currently surrounds the use of artificial intelligence (AI) to process large data sets, but manufacturers struggle to understand how AI can be applied to improve manufacturing system performance. The gap lies in the lack of good information governance practices for manufacturing. This paper defines information governance in the manufacturing context as the set of principles that allows for consistent, repeatable, and trustworthy processing and use of data. The paper identifies three foundations for good information governance that are needed in the manufacturing environment—data quality, semantic context, and system context—and reviews the surrounding and evolving body of work. The work includes a broad base of standard methods that combine to create reusable information from raw data formats. An example from an additive manufacturing case study is used to show how those detailed specifications create the governance needed to build trust in the systems.

Author Information

Morris, K. C.
Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
Lu, Yan
Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
Frechette, Simon
Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
Pages: 19
Price: Free
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Details
Stock #: SSMS20190041
ISSN: 2520-6478
DOI: 10.1520/SSMS20190041