Data Processing in Industrie 4.0

Data Analysis and Knowledge Management in Industrie 4.0

Abstract

The pressure on companies to increase their flexibility and efficiency in manufacturing is constantly increasing. Factory managers therefore need to be able to obtain information in real-time across physical production systems for better decision making. Transparency on a production- and strategic level, for example, offers the advantage of being able to respond more quickly to volatile demand (time-to-market) and helps in reducing lead- and down-times. This can lead to a significant production gain and competitive advantage. Current approaches are challenged to bring results from the IoT world to decision makers in an appropriate manner. We introduce data models that serve as a mediator to create a better understanding between factory owners and data analysts. Particular challenges lie in the orchestration of the complex process steps, the vertical transparency of information, as well as in mutually contradictory optimization calculi (e.g., cost, speed, quality, sustainability). Due to better communication between factory managers, data analysts and people working at the line-side, the previously mentioned configurations can be implemented more transparently and consequently more efficiently.

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Notes

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    http://www.disrupt-project.eu/.

  2. 2.

    http://www.boc-group.com.

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    http://www.boc-group.com.

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    https://www.adoxx.org/live/home.

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    https://www.adoxx.org/live/olive.

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    http://austria.omilab.org/psm/content/bdds/info.

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    http://www.orbeet.eu.

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Correspondence to Frank Werner.

Additional information

The work on this paper is funded mainly by the European Commission through the DISRUPT project (H2020 FOF-11-2016, RIA project n. 723541, 2016-2019). The authors would also like to thank the contributions of the different partners of the DISRUPT project.

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Werner, F., Woitsch, R. Data Processing in Industrie 4.0. Datenbank Spektrum 18, 15–25 (2018). https://doi.org/10.1007/s13222-018-0277-x

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Keywords

  • Smart data management
  • Complex event processing
  • Data analytics
  • Industrial Internet of Things (IIoT)