Towards Planning and Control of Business Processes based on Event-Based Predictions

Julian Krumeich; Sven Jacobi; Dirk Werth; Peter Loos(2014)
In: Business Information Systems (BIS2014), Larnaca, Cyprus, May 2014, In: 176, Springer, Berlin, S. 38-49, DOI:

Link zur Publikation:


To keep up with increasing market demands in global competition, companies are forced to dynamically adapt each of their business process executions to currently present business situations. 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, are a decisive step ahead competitors. The paper at hand exploits potentials of predictive analytics on big data aiming at event-based forecasts and proactive control of business processes. In doing so, the paper outlines—based on a case study of a steel producing company—which production-related data is currently collected forming a potential foundation for accurate forecasts. However, without dedicated methods 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 on big data analytics.