Machine learning in business process management: A systematic literature review
Sven Weinzierl, Sandra Zilker, Sebastian Dunzer, Martin Matzner
TL;DR
The paper offers the first exhaustive systematic review of how ML is applied across the BPM lifecycle, mapping data inputs, modelling techniques, and tasks to lifecycle phases. It reveals a strong emphasis on event-log–driven tasks such as process discovery, analysis, and monitoring, with deep learning and self-/semi-supervised approaches frequently employed for representation learning and pattern detection. It also identifies ten key findings, including data limitations, the value of domain knowledge, and the dominance of benchmark event logs, and it presents a ten-point research agenda addressing data sources, context, new data forms, and ethical considerations. Practically, the work equips researchers and practitioners with a structured knowledge base and an interactive coding table to locate relevant work, enabling more targeted development and transfer of ML applications in BPM.
Abstract
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.