Weinzierl S, Zilker S, Dunzer S, Matzner M (2024)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2024
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications
are used to analyse and improve processes efficiently. Three frequent examples of using ML
are providing decision support through predictions, discovering accurate process models, and
improving resource allocation. This paper organises the body of knowledge on ML in BPM. We
extract BPM tasks from different literature streams, summarise them under the phases of a process’s lifecycle, explain how ML helps perform these tasks and identify technical commonalities
in ML implementations across tasks. This study is the first exhaustive review of how ML has
been used in BPM. We hope that it can open the door for a new era of cumulative research by
helping researchers to identify relevant preliminary work and then combine and further develop
existing approaches in a focused fashion. Our paper helps managers and consultants to find ML
applications that are relevant in the current project phase of a BPM initiative, like redesigning
a business process. We also offer – as a synthesis of our review – a research agenda that spreads
ten avenues for future research, including applying novel ML concepts like federated learning,
addressing less regarded BPM lifecycle phases like process identification, and delivering ML
applications with a focus on end-users.
APA:
Weinzierl, S., Zilker, S., Dunzer, S., & Matzner, M. (2024). Machine Learning in business process management: A systematic literature review (forthcoming). Expert Systems With Applications.
MLA:
Weinzierl, Sven, et al. "Machine Learning in business process management: A systematic literature review (forthcoming)." Expert Systems With Applications (2024).
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