Machine learning in business process management: A systematic literature review

TL;DR

This systematic review comprehensively examines how machine learning techniques are applied across various phases of business process management, highlighting current practices, technical commonalities, and future research directions.

Contribution

It is the first exhaustive review that organizes ML applications in BPM, providing a structured overview and identifying key research opportunities.

Findings

ML supports decision prediction, process discovery, and resource optimization.

The review identifies common ML implementation techniques in BPM.

It proposes a research agenda with ten future directions.

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.