Weinzierl S, Dunzer S, Tenschert J, Zilker S, Matzner M (2021)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2021
Pages Range: 1-12
Conference Proceedings Title: Proceedings of the 29th European Conference on Information Systems
Business processes run at the core of an organisation’s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the
optimised process still occur and may potentially impede efficiency in process executions. Conformance
checking can provide valuable insights regarding past process deviations, but it cannot identify deviations
before they occur. Outcome-oriented predictive business process monitoring (PBPM) provides a set of
methods to predict process outcomes, e. g. key performance indicators. We propose an outcome-oriented
PBPM method for predictive deviation monitoring using conformance checking and deep learning to
draw the most out of the two domains. By leveraging early intervention, the method supports the proactive handling of deviations, i.e., inserted and missing events in process instances, to reduce their potential
harm. Our evaluation shows that the method can predict business process deviations with high predictive
quality, particularly for processes with fewer variants.
APA:
Weinzierl, S., Dunzer, S., Tenschert, J., Zilker, S., & Matzner, M. (2021). Predictive business process deviation monitoring. In Proceedings of the 29th European Conference on Information Systems (pp. 1-12). Marrakesch, MA.
MLA:
Weinzierl, Sven, et al. "Predictive business process deviation monitoring." Proceedings of the European Conference on Information Systems, Marrakesch 2021. 1-12.
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