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

Julian Krumeich, Sven Jacobi, Dirk Werth, Peter Loos

In: Witold Abramowicz , Angelika Kokkinaki (Hrsg.). Proceedings of 17th International Conference on Business Information Systems. International Conference on Business Information Systems (BIS-14) Big Data: problems solved and remaining challenges May 21-23 Larnaca Cyprus Seiten 38-49 Lecture Notes in Business Information Processing (LNBIP) 176 Springer Berlin 5/2014.


To keep up with increasing market demands in global competition, companies are forced to dynamically adapt each of their business process executions to individual business situations. Companies that are able to analyze the current state of their business processes, forecast its most optimal progress and proactively control them based on the derived knowledge, are an essential step ahead competitors. The paper at hand exploits the potentials through the usage 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 large steel producing company—which production-related data can be collected by the applied sensor technology at present; hence, forming a potential foundation for accurate forecasts. However, without dedicated methods of big data analytics, the 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.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence