iPRODICT – Intelligent Process Prediction based on Big Data Analytics

Nijat Mehdiyev, Andreas Emrich, Björn Stahmer, Peter Fettke, Peter Loos (Hrsg.)

Business Process Management (BPM-17) Industry Track September 10-15 Barcelona Spain BPM Springer Cham 9/2017.


The major purpose of the iPRODICT research project is to operationalize in-dustrial internet of things driven predictive and prescriptive analytics by em-bedding it to the operational processes. Particularly, within an interdiscipli-nary team of researchers and industry experts, we investigate an integration of the diverse technologies to enable real time sensor data driven decision making for process improvements and optimization in the process industry. The case study concentrates on adaptation and optimization of both manu-facturing and business processes by analyzing the quality of the semi-finished steel products proactively based on the sensor data obtained from the continuous casting process and chemical properties of the steel. In the underlying paper, we discussed three business process management specific use cases in the sensor-driven process industry, namely (i) business process instance adaptation, (ii) business process instance-to-instance adaptation and optimization and (iii) business process instance-to-model adaptation. Fur-thermore, we discuss the components of the proposed predictive enterprise solution and their dependencies briefly and provide an insight to the chal-lenges and lessons learned over the diverse stages of the case study.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence