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Analysis of Business Process Batching Using Causal Event Models

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Process Mining Workshops (ICPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 406))

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Abstract

Process mining supports business process management with operational insights extracted from event logs. A key challenge for process mining is that operational processes in production and logistics often include batching and unbatching, e.g., to delivery several packages using one truck tour. Such n:m relations blur the notion of a process instance and make the causality between events difficult to trace. In this paper, we address this research problem by introducing causal event models that capture batching behavior accurately. To this end, we construct conflict-free prime event structures for event instances of the event log, and devise various analysis techniques on top of them. We implemented the techniques in a tool and run in real data of a manufacturing company with various 1:n and n:1 relations in their production process showing the potential of our approach.

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Authors and Affiliations

  1. Institute for Information Business, Vienna University of Economics and Business (WU), Vienna, Austria

    Philipp Waibel, Christian Novak, Saimir Bala, Kate Revoredo & Jan Mendling

Authors
  1. Philipp Waibel
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  2. Christian Novak
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  3. Saimir Bala
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  4. Kate Revoredo
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  5. Jan Mendling
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Corresponding author

Correspondence to Philipp Waibel .

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Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Sander Leemans

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

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Waibel, P., Novak, C., Bala, S., Revoredo, K., Mendling, J. (2021). Analysis of Business Process Batching Using Causal Event Models. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72692-8

  • Online ISBN: 978-3-030-72693-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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