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Predictive End-to-End Enterprise Process Network Monitoring

Author

Listed:
  • Felix Oberdorf

    (Julius-Maximilians-University Würzburg)

  • Myriam Schaschek

    (Julius-Maximilians-University Würzburg)

  • Sven Weinzierl

    (Friedrich-Alexander University Erlangen-Nürnberg)

  • Nikolai Stein

    (Julius-Maximilians-University Würzburg)

  • Martin Matzner

    (Friedrich-Alexander University Erlangen-Nürnberg)

  • Christoph M. Flath

    (Julius-Maximilians-University Würzburg)

Abstract

Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method’s utility for organizations.

Suggested Citation

  • Felix Oberdorf & Myriam Schaschek & Sven Weinzierl & Nikolai Stein & Martin Matzner & Christoph M. Flath, 2023. "Predictive End-to-End Enterprise Process Network Monitoring," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(1), pages 49-64, February.
    • Handle: RePEc:spr:binfse:v:65:y:2023:i:1:d:10.1007_s12599-022-00778-4
    DOI: 10.1007/s12599-022-00778-4
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    References listed on IDEAS

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    1. Nijat Mehdiyev & Joerg Evermann & Peter Fettke, 2020. "A Novel Business Process Prediction Model Using a Deep Learning Method," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(2), pages 143-157, April.
    2. Michael zur Muehlen & Robert Shapiro, 2015. "Business Process Analytics," International Handbooks on Information Systems, in: Jan vom Brocke & Michael Rosemann (ed.), Handbook on Business Process Management 2, edition 2, pages 243-263, Springer.
    3. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    4. Julia Eggers & Andreas Hein & Markus Böhm & Helmut Krcmar, 2021. "No Longer Out of Sight, No Longer Out of Mind? How Organizations Engage with Process Mining-Induced Transparency to Achieve Increased Process Awareness," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(5), pages 491-510, October.
    5. Rafael Lorenz & Julian Senoner & Wilfried Sihn & Torbjørn Netland, 2021. "Using process mining to improve productivity in make-to-stock manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4869-4880, August.
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