Advanced Planning and Control of Manufacturing Processes in Steel Industry through Big Data Analytics: Case Study and Architecture Proposal

Julian Krumeich; Dirk Werth; Jens Schimmelpfennig; Sven Jacobi; Peter Loos(2014)
In: Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), IEEE Conference on Big Data, Washington D.C., USA, October 2014, IEEE Xplore® Digital Library, DOI: 10.1109/BigData.2014.7004408

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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