Article in Proceedings INPROC-2012-15

BibliographyGröger, Christoph; Niedermann, Florian; Mitschang, Bernhard: Data Mining-driven Manufacturing Process Optimization.
In: Ao, S. I. (ed.); Gelman, L. (ed.); Hukins, D. W. L. (ed.); Hunter, A. (ed.); Korsunsky, A. M. (ed.): Proceedings of the World Congress on Engineering 2012 Vol III, WCE 2012, 4 – 6 July, 2012, London, U.K..
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 1475-1481, english.
Newswood, July 2012.
ISBN: 978-988-19252-2-0.
Article in Proceedings (Conference Paper).
CorporationInternational Association of Engineers (IAENG)
CR-SchemaH.2.8 (Database Applications)
J.1 (Administration Data Processing)
KeywordsAnalytics; Data Mining; Decision Support; Process Optimization

High competitive pressure in the global manufacturing industry makes efficient, effective and continuously improved manufacturing processes a critical success factor. Yet, existing analytics in manufacturing, e. g., provided by Manufacturing Execution Systems, are coined by major shortcomings considerably limiting continuous process improvement. In particular, they do not make use of data mining to identify hidden patterns in manufacturing-related data. In this article, we present indication-based and pattern-based manufacturing process optimization as novel data mining approaches provided by the Advanced Manufacturing Analytics Platform. We demonstrate their usefulness through use cases and depict suitable data mining techniques as well as implementation details.

Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
Entry dateMay 4, 2012
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