Abstract
Nowadays, large amounts of data are generated in the manufacturing industry. In order to make these data usable for data-driven analysis tasks such as smart data discovery, a suitable system needs to be developed in a multi-stage process - starting with data acquisition and storage, data processing and analysis, suitable definition of use cases and project goals, and finally utilization and integration of the analysis results to the productive system. Experience from different industrial projects shows that close interaction between all these sub-tasks over the whole process and intensive and steady knowledge transfer between domain experts and data experts are essential for successful implementation. This paper proposes a stakeholder-aware methodology for developing data-driven analytics use-cases by combining an optimal project-development strategy with a generic data analytics infrastructure. The focus lies on including all stakeholders in every part of the use-case development. Using the example of a concrete industry project where we work towards a system for monitoring process stability of the whole machinery at the customer side, we show best practice guidance and lessons learned for this kind of digitalization process in industry.
The research reported in this paper has been funded by BMK, BMDW, and the State of Upper Austria in the frame of the COMET Programme managed by FFG.
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Himmelbauer, J., Mayr, M., Luftensteiner, S. (2022). From Data to Decisions - Developing Data Analytics Use-Cases in Process Industry. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_8
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DOI: https://doi.org/10.1007/978-3-031-14343-4_8
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