Predictive End-to-End Enterprise Process Network Monitoring

Authors

Felix Oberdorf, Bayerische Julius-Maximilians-Universitat WurzburgFollow
Myriam Schaschek, Bayerische Julius-Maximilians-Universitat Wurzburg
Sven Weinzierl, Friedrich-Alexander-University of Erlangen-Nürnberg
Nikolai Stein, Bayerische Julius-Maximilians-Universitat Wurzburg
Martin Matzner, Friedrich-Alexander University of Erlangen-Nürnberg
Christoph M. Flath, Julius-Maximilians-University Würzburg

Document Type

Research Paper

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.

Recommended Citation

Oberdorf, Felix; Schaschek, Myriam; Weinzierl, Sven; Stein, Nikolai; Matzner, Martin; and Flath, Christoph M. (2023) "Predictive End-to-End Enterprise Process Network Monitoring," Business & Information Systems Engineering: Vol. 65: Iss. 1, 49-64.
Available at: https://aisel.aisnet.org/bise/vol65/iss1/4