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Combining Process Mining and Machine Learning for Lead Time Prediction in High Variance Processes

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Production at the leading edge of technology (WGP 2020)

Part of the book series: Lecture Notes in Production Engineering ((LNPE))

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Abstract

Machine learning offers a high potential for the prediction of manufacturing lead times. In practical operations the lack of defined processes and high-quality input data are a major obstacle for the use of machine learning. The method of process mining creates a better transparency of such workflows and enriches related data. This paper develops a method, which combines the benefits of machine learning and process mining with the goal of high accuracy lead time prediction. The method is focused on high variance processes and verified with a case study containing real industrial data from heavy engine assembly processes.

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Acknowledgements

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.

The authors would also like to thank MAN Truck & Bus SE for their kind support.

Author information

Authors and Affiliations

  1. Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Campus-Boulevard 30, 52074, Aachen, Germany

    M. Welsing, J. Maetschke, K. Thomas, A. Gützlaff & G. Schuh

  2. MAN Truck & Bus SE, Vogelweiherstraße 33, 90441, Nürnberg, Germany

    S. Meusert

Authors
  1. M. Welsing
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  2. J. Maetschke
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  3. K. Thomas
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  4. A. Gützlaff
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  5. G. Schuh
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  6. S. Meusert
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Corresponding author

Correspondence to M. Welsing .

Editor information

Editors and Affiliations

  1. Institute of Forming Technology and Machines, Garbsen, Germany

    Bernd-Arno Behrens

  2. Institute of Manufacturing Science and Engineering, Dresden, Germany

    Alexander Brosius

  3. Institute of Production Management and Technology, Hamburg, Germany

    Wolfgang Hintze

  4. Institute of Mechatronic Engineering, Dresden, Germany

    Steffen Ihlenfeldt

  5. Laboratorium for Manufacturing Technology, Hamburg, Germany

    Jens Peter Wulfsberg

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Welsing, M., Maetschke, J., Thomas, K., Gützlaff, A., Schuh, G., Meusert, S. (2021). Combining Process Mining and Machine Learning for Lead Time Prediction in High Variance Processes. In: Behrens, BA., Brosius, A., Hintze, W., Ihlenfeldt, S., Wulfsberg, J.P. (eds) Production at the leading edge of technology. WGP 2020. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62138-7_53

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  • DOI: https://doi.org/10.1007/978-3-662-62138-7_53

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  • Print ISBN: 978-3-662-62137-0

  • Online ISBN: 978-3-662-62138-7

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