Author:
Oberdorf Felix,Schaschek Myriam,Weinzierl Sven,Stein Nikolai,Matzner Martin,Flath Christoph M.
Abstract
AbstractEver-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.
Funder
Julius-Maximilians-Universität Würzburg
Publisher
Springer Science and Business Media LLC
Reference98 articles.
1. Adams JN, Zelst SJv, Quack L, Hausmann K, van der Aalst WM, Rose T (2021) A framework for explainable concept drift detection in process mining. In: Proceedings of the international conference on business process management. Springer, Heidelberg: 400–416
2. Armengaud E, Fruhwirth M, Rothbart M, Weinzerl M, Zembacher G (2020) Digitalization as an opportunity to remove silo-thinking and enable holistic value creation. Syst Eng Automot Powertrain Dev, 1–28
3. Beheshti A, Benatallah B, Motahari-Nezhad HR (2018) ProcessAtlas: a scalable and extensible platform for business process analytics. Softw Pract Exp 48(4):842–866
4. Benatallah B, Sakr S, Grigori D, Motahari-Nezhad HR, Barukh MC, Gater A, Ryu SH et al (2016) Process analytics: concepts and techniques for querying and analyzing process data. Springer Cham
5. Benscoter B (2012) How to identify and analyze problems in your organization: 290–294. https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118364727.ch29
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