Abstract
Nowadays, manufacturers are increasingly able to collect and analyze data from sensors on manufacturing equipment, and also from other types of machinery, such as smart meters, pipelines, delivery trucks, etc. Nevertheless, many manufacturers are not yet ready to use analytics beyond a tool to track historical performance. However, just knowing what happened and why it happened does not use the full potential of the data and is not sufficient anymore. Manufacturers need to know what happens next and what actions to take in order to get optimal results. It is a challenge to develop advanced analytics techniques including machine learning and predictive algorithms to transform data into relevant information for gaining useful insights to take appropriate action. In the proposed research we target new analytic methods and tools that make insights not only more understandable but also actionable by decision makers. The latter requires that the results of data analytics have an immediate effect on the processes of the manufacturer. Thereby, data analytics has the potential to improve industrial processes by reducing maintenance costs, avoiding equipment failures and improving business operations. Accordingly, the overall objective of this project is to develop a set of tools — including algorithms, analytic machinery, planning approaches and visualizations — for industrial process improvements based on data analytics.
Citation
Stefan Thalmann,
Juergen Mangler,
Tobias Schreck,
Christian Huemer,
Marc
Streit,
Florian Pauker,
Georg Weichhart,
Stefan Schulte,
Christian Kittl,
Christoph Pollak,
Matej Vukovic,
Gerti Kappel,
M. Gashi,
S. Rinderle-Ma,
J. Suschnigg,
N. Jekic,
Stefanie Lindstaedt
Data Analytics for Industrial Process Improvement A Vision Paper
IEEE Conference on Business Informatics (CBI '18),
doi:10.1109/CBI.2018.10051, 2018.
BibTeX
@article{2018_cbi_industrial-process-management,
title = {Data Analytics for Industrial Process Improvement A Vision Paper},
author = {Stefan Thalmann and Juergen Mangler and Tobias Schreck and Christian Huemer and Marc Streit and Florian Pauker and Georg Weichhart and Stefan Schulte and Christian Kittl and Christoph Pollak and Matej Vukovic and Gerti Kappel and M. Gashi and S. Rinderle-Ma and J. Suschnigg and N. Jekic and Stefanie Lindstaedt},
journal = {IEEE Conference on Business Informatics (CBI '18)},
doi = {10.1109/CBI.2018.10051},
year = {2018}
}
Acknowledgements
This research is done truly joint by the two Comet K1- Research Centers ProFuture and Center for Digital Production (CDP). ProFuture (Contract Nr. 854184) and CDP (Contract Nr. 854187) are funded within the Austrian COMET ProgramCompetence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and the Provinces of Upper Austria and Styria (for ProFuture) and the Provinces of Vienna, Lower Austria, and Vorarlberg (for CDP). COMET is managed by the Austrian Research Promotion Agency FFG.
