Predictive End-to-End Enterprise Process Network Monitoring

Felix Oberdorf (), Myriam Schaschek, Sven Weinzierl, Nikolai Stein, Martin Matzner and Christoph M. Flath
Additional contact information
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

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2023, vol. 65, issue 1, No 4, 49-64

Abstract: 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.

Keywords: Predictive process analytics; Predictive process monitoring; Deep learning; Machine learning; Neural network; Business process anagement; Process mining (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12599-022-00778-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:binfse:v:65:y:2023:i:1:d:10.1007_s12599-022-00778-4

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/12599

DOI: 10.1007/s12599-022-00778-4

Access Statistics for this article

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK is currently edited by Martin Bichler

More articles in Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK from Springer, Gesellschaft für Informatik e.V. (GI)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().