NDLI: Advanced planning and control of manufacturing processes in steel industry through big data analytics: Case study and architecture proposal

Content Provider	IEEE Xplore Digital Library
Author	Krumeich, J. Werth, D. Loos, P. Schimmelpfennig, J. Jacobi, S.
Copyright Year	2014
Description	Author affiliation: Software AG, Saarbrucken, Germany (Schimmelpfennig, J.) \|\| Saarstahl AG, Volklingen, Germany (Jacobi, S.) \|\| German Res. Center for Artificial Intell. (DFKI GmbH) Saarbrucken, Saarbrucken, Germany (Krumeich, J.; Werth, D.; Loos, P.)
Abstract	Enterprises in today's globalized world are compelled to react on threats and opportunities in a highly flexible manner. Hence, companies that are able to analyze the current state of their business processes, forecast their most optimal progresses and with this proactively control them will have a decisive competitive advantage. Technological progress in sensor technology has boosted real-time situation awareness, especially in manufacturing operations. The paper at hands examines, based on a case study stemming from the steel manufacturing industry, which production-related data is collectable using state of the art sensors forming a basis for a detailed situation awareness and for deriving accurate forecasts. However, analyses of this data point out that dedicated big data analytics approaches are required to utilize the full potential out of it. By proposing an architecture for predictive process planning and control systems, the paper intends to form a working and discussion basis for further research and implementation efforts in big data analytics.
Starting Page	16
Ending Page	24
File Size	1621447
Page Count	9
File Format	PDF
ISBN	9781479956661
DOI	10.1109/BigData.2014.7004408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industries Manufacturing processes Ontology Event-driven business process management Process control Business process intelligence Complex event processing Business process forecast and simulation Big data Business activity monitoring Steel Predictive analytics
Content Type	Text
Resource Type	Article

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