Business & Information Systems Engineering

, Volume 58, Issue 4, pp 261–280 | Cite as

Prescriptive Control of Business Processes

New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry
  • Julian KrumeichEmail author
  • Dirk Werth
  • Peter Loos
Research Paper


This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.


Predictive analytics Complex event processing Prescriptive analytics Event-driven business process management Big data Process industry 



This research was funded in part by the German Federal Ministry of Education and Research under grant numbers 01IS12050 (project IDENTIFY) and 01IS14004A (project iPRODICT).


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Copyright information

© Springer Fachmedien Wiesbaden 2015

Authors and Affiliations

  1. 1.Institute for Information Systems (IWi)German Research Center for Artificial Intelligence (DFKI GmbH)SaarbrückenGermany

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