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Reinforcement learning-supported ab testing of business process improvements: an industry perspective
Technische Universit¨at Berlin, Berlin, Germany; Technische Universität München, Munich, Germany.ORCID iD: 0000-0002-2547-6780
SAP Signavio, Berlin, Germany. (Interactive and Intelligent Systems Group)ORCID iD: 0000-0002-6458-2252
Technische Universität München, Munich, Germany.
Technische Universität München, Munich, Germany; Fraunhofer Gesellschaft, Munich, Germany.ORCID iD: 0000-0002-4833-5921
2023 (English)In: Enterprise, business-process and information systems modeling: 24th International Conference, BPMDS 2023, and 28th International Conference, EMMSAD 2023, Zaragoza, Spain, June 12–13, 2023, Proceedings / [ed] Han van der Aa; Dominik Bork; Henderik A. Proper; Rainer Schmidt, Cham: Springer Nature, 2023, p. 12-26Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer Nature, 2023. p. 12-26
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 479
Keywords [en]
Business Process Improvement, Process Redesign, Reinforcement Learning, AB Testing, Grounded Theory, Delphi Study
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-209530DOI: 10.1007/978-3-031-34241-7_2ISBN: 978-3-031-34240-0 (print)ISBN: 978-3-031-34241-7 (electronic)OAI: oai:DiVA.org:umu-209530DiVA, id: diva2:1765626
Conference
BPMDS2023, International Conference on Business Process Modeling, Development and Support, Zaragoza, Spain, June 12-13, 2023
Available from: 2023-06-11 Created: 2023-06-11 Last updated: 2023-06-12Bibliographically approved

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Kampik, Timotheus

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Kurz, Aaron FriedrichKampik, TimotheusWeber, Ingo
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Total: 248 hits
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