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S. Thalmann, J. Mangler, T. Schreck, C. Huemer, M Streit, F. Pauker, G. Weichhart, S. Schulte, C. Kittl, C. Pollak, M. Vukovic, G. Kappel, M. Gashi, S. Rinderle-Ma, J. Suschnigg, N. Jekic, S. Lindstaedt:
"Data Analytics for Industrial Process Improvement - A Vision Paper";
Talk: 2018 IEEE 20th Conference on Business Informatics (CBI), Vienna; 2018-07-11 - 2018-07-14; in: "2018 IEEE 20th Conference on Business Informatics (CBI)", IEEE Computer Society, (2018), ISBN: 978-1-5386-7016-3; 92 - 96.

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.

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.

Production , Data analysis , Data visualization , Tools , Automation , Data models, Data Analytics , Visual Analytics , Process Management , Industry 4.0

http://dx.doi.org/10.1109/CBI.2018.10051

https://publik.tuwien.ac.at/files/publik_276777.pdf