Please use this identifier to cite or link to this item: doi:10.22028/D291-33412
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Title: iPRODICT – Intelligent Process Prediction based on Big Data Analytics
Author(s): Mehdiyev, Nijat
Emrich, Andreas
Stahmer, Björn
Fettke, Peter
Loos, Peter
Editor(s): Brambilla, Marco
Hildebrandt, Thomas
Language: English
Title: BPM-Industry 2017, BPM 2017 Industry Track : proceedings of the BPM 2017 Industry Track, co-located with the 15th International Conference on Business Process Management (BPM 2017) : Barcelona, Spain, September 10-15, 2017
Pages: 11
Publisher/Platform: RWTH Aachen
Year of Publication: 2017
Place of publication: Aachen
Title of the Conference: BPM 2017
Place of the conference: Barcelona, Spain
Publikation type: Conference Paper
Abstract: 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.
URL of the first publication:
Link to this record: hdl:20.500.11880/30730
Date of registration: 24-Feb-2021
Notes: CEUR workshop proceedings ; vol-1985
Faculty: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Department: HW - Wirtschaftswissenschaft
Professorship: HW - Prof. Dr. Peter Loos
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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