TY - GEN
T1 - Reinforcement Learning-Supported AB Testing of Business Process Improvements
T2 - 24th International Conference on Business Process Modeling, Development, and Support, BPMDS 2023 and 28th International Conference on Exploring Modeling Methods for Systems Analysis and Development, EMMSAD 2023
AU - Kurz, Aaron Friedrich
AU - Kampik, Timotheus
AU - Pufahl, Luise
AU - Weber, Ingo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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 and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing requirements, risks, opportunities, and more aspects of the AB-BPM methodology and supporting tools. Our qualitative analysis combines grounded theory with a Delphi study, including semi-structured interviews and multiple follow-up surveys with a panel of ten business process management experts. The main findings indicate a need for human control during reinforcement learning-driven experiments, the importance of aligning the methodology culturally and organizationally with the respective setting, and the necessity of an integrated process execution platform.
AB - 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 and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing requirements, risks, opportunities, and more aspects of the AB-BPM methodology and supporting tools. Our qualitative analysis combines grounded theory with a Delphi study, including semi-structured interviews and multiple follow-up surveys with a panel of ten business process management experts. The main findings indicate a need for human control during reinforcement learning-driven experiments, the importance of aligning the methodology culturally and organizationally with the respective setting, and the necessity of an integrated process execution platform.
KW - AB Testing
KW - Business Process Improvement
KW - Delphi Study
KW - Grounded Theory
KW - Process Redesign
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85173098003&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34241-7_2
DO - 10.1007/978-3-031-34241-7_2
M3 - Conference contribution
AN - SCOPUS:85173098003
SN - 9783031342400
T3 - Lecture Notes in Business Information Processing
SP - 12
EP - 26
BT - Enterprise, Business-Process and Information Systems Modeling - 24th International Conference, BPMDS 2023, and 28th International Conference, EMMSAD 2023, Proceedings
A2 - van der Aa, Han
A2 - Bork, Dominik
A2 - Proper, Henderik A.
A2 - Schmidt, Rainer
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 June 2023 through 13 June 2023
ER -