Work

Business process improvement with AB testing and reinforcement learning: grounded theory-based industry perspectives

Year: 2024

Type: article

Abstract: In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing—a DevOps practice—and reinforcement learning to increase the speed and quality of business process improvement efforts. In this paper, we provide an industry perspective on this approach, assessing prerequisites, suitability, requirements, risks, and additional aspects of the AB-BPM methodology and supporting tools. Our qualitative study follows the grounded theory research methodology, including 16 semi-structured interviews with BPM practitioners. The main findings indicate: (1) a need for expert control during reinforcement learning-driven experiments in production, (2) the importance of involving the participants and aligning the method culturally with the respective setting, (3) the necessity of an integrated process execution environment, and (4) the long-term potential of the methodology for effective and efficient validation of algorithmically (co-)created business process variants, and their continuous management.

Source: Software & Systems Modeling

DOI: 10.1007/s10270-024-01229-2

Authors: Aaron Friedrich Kurz, Timotheus Kampik, Luise Pufahl, Ingo Weber

Institutions: Systems, Applications & Products in Data Processing (Germany), Technical University of Munich, Fraunhofer-Gesellschaft

Language: English

Topic: Business Process Modeling and Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

Funder: Technische Universität München