iPRODICT - Intelligent Process Prediction based on Big Data Analytics

The major purpose of the iPRODICT research project is to operationalize industrial internet of things driven predictive and prescriptive analytics by embedding it to the operational processes. Particularly, within an interdisciplinary team of researchers and industry experts, we investigate an integration of 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 manufacturing 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 instanceto-instance adaptation and optimization and (iii) business process instance-tomodel adaptation. Furthermore, we discuss the components of the proposed predictive enterprise solution and their dependencies briefly and provide an insight to the challenges and lessons learnt over the diverse stages of the case study.