Prescriptive Control of Business Processes - New Potentials through Predictive Analytics on Big Data in the Process Manufacturing Industry

Julian Krumeich; Dirk Werth; Peter Loos (2015)
In: Business & Information Systems Engineering (BISE), In: 58, 4, Springer, S. 261-280, DOI: 10.1007/s12599-015-0412-2

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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 on 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 of 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 – is a typical example of the process industry – the work at hand outlines which data is becoming 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.