Big Data Analytics for Predictive Manufacturing Control - A Case Study from Process Industry

Julian Krumreich; Sven Jacobi; Dirk Werth; Peter Loos(2014)
In: IEEE International Congress on Big Data (BigData Congress), S. 530-537

Link zur Publikation:


Nowadays, companies are more than ever forced to dynamically adapt their business process executions to currently existing business situations in order to keep up with increasing market demands in global competition. Companies that are able to analyze the current state of their processes, forecast its most optimal progress and proactively control them based on reliable predictions will be a decisive step ahead competitors. The paper at hand exploits potentials through predictive analytics on big data aiming at event-based predictions and thereby enabling proactive control of business processes. In doing so, the paper particularly focus production processes in analytical process manufacturing industries and outlines-based on a case study at Saarstahl AG, a large German steel producing company-which production-related data is currently collected forming a potential foundation for accurate forecasts. However, without dedicated approaches of big data analytics, the sample company cannot utilize the potential of already available data for a proactive process control. Hence, the article forms a working and discussion basis for further research in big data analytics by proposing a general system architecture