Abstract
In the age of “Industry 4.0”, the rising amount of data from production environments is primarily used to control the operational production environment. However, companies frequently do not exploit the data’s potential for strategic and tactical decision making. A possible reason is that many data analysis tools follow a method-centric perspective, which is not compatible with the problem-centric view of the tasks of a particular department. This dissertation project investigates the theoretical and practical improvement of data analysis processes in industrial corporations. To overcome these often distinct perspectives and foster the implementation of state-of-the-art methods of data analysis such as data mining, we propose the standardization of data analysis tasks in industrial corporations by construction of a reference model that can help building data analysis tools. As a first step, we survey different types of analysis tasks arising in the environment of the automotive industry, namely the AUDI AG.
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Eckert, S., Ehmke, J.F. (2017). Classification of Data Analysis Tasks for Production Environments. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-52464-1_37
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DOI: https://doi.org/10.1007/978-3-319-52464-1_37
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