Predictive end-to-end enterprise process network monitoring

Oberdorf F, Schaschek M, Weinzierl S, Stein N, Matzner M, Flath C (2023)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2023

Journal

Business & Information Systems Engineering Springer Verlag (Germany)

DOI: 10.1007/s12599-022-00778-4

Open Access Link: https://link.springer.com/article/10.1007/s12599-022-00778-4

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. This 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.

Authors with CRIS profile

Sven Weinzierl Lehrstuhl für Digital Industrial Service Systems Martin Matzner Lehrstuhl für Digital Industrial Service Systems

Involved external institutions

Julius-Maximilians-Universität Würzburg DE Germany (DE)

How to cite

APA:

Oberdorf, F., Schaschek, M., Weinzierl, S., Stein, N., Matzner, M., & Flath, C. (2023). Predictive end-to-end enterprise process network monitoring. Business & Information Systems Engineering. https://dx.doi.org/10.1007/s12599-022-00778-4

MLA:

Oberdorf, Felix, et al. "Predictive end-to-end enterprise process network monitoring." Business & Information Systems Engineering (2023).

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