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Rethinking business process simulation: A utility-based evaluation framework
Özdemir, Konrad
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Kirchdorfer, Lukas
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Amiri Elyasi, Keyvan
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van der Aa, Han
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Stuckenschmidt, Heiner
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DOI:
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https://doi.org/10.1007/978-3-032-02929-4_8
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URL:
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https://www.springerprofessional.de/en/rethinking-...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2026
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Book title:
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Business Process Management Forum : BPM 2025 Forum, Seville, Spain, August 31 - September 5, 2025, Proceedings
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The title of a journal, publication series:
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Lecture Notes in Business Information Processing : LNBIP
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Volume:
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564
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Conference title:
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Business Process Management Forum (BPM 2025 Forum)
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Location of the conference venue:
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Sevilla, Spain
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Date of the conference:
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31.08.-05.09.2025
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Publisher:
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Senderovich, Arik
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Cabanillas, Cristina
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Vanderfeesten, Irene
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Reijers, Hajo A.
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Place of publication:
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Berlin [u.a.]
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Publishing house:
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Springer
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ISBN:
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978-3-032-02928-7 , 978-3-032-02929-4
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ISSN:
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1865-1348 , 1865-1356
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Subject:
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004 Computer science, internet
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Keywords (English):
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process simulation , process mining , deep learning
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Abstract:
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Business process simulation (BPS) is a key tool for analyzing and optimizing organizational workflows, supporting decision-making by estimating the impact of process changes. The reliability of such estimates depends on the ability of a BPS model to accurately mimic the process under analysis, making rigorous accuracy evaluation essential. However, the state-of-the-art approach to evaluating BPS models has two key limitations. First, it treats simulation as a forecasting problem, testing whether models can predict unseen future events. This fails to assess how well a model captures the as-is process, particularly when process behavior changes from train to test period. Thus, it becomes difficult to determine whether poor results stem from an inaccurate model or the inherent complexity of the data, such as unpredictable drift. Second, the evaluation approach strongly relies on Earth Mover’s Distance-based metrics, which can obscure temporal patterns and thus yield misleading conclusions about simulation quality. To address these issues, we propose a novel framework that evaluates simulation quality based on its ability to generate representative process behavior. Instead of comparing simulated logs to future real-world executions, we evaluate whether predictive process monitoring models trained on simulated data perform comparably to those trained on real data for downstream analysis tasks. Empirical results show that our framework not only helps identify sources of discrepancies but also distinguishes between model accuracy and data complexity, offering a more meaningful way to assess BPS quality.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Özdemir, Konrad
;
Kirchdorfer, Lukas
;
Amiri Elyasi, Keyvan
;
van der Aa, Han
;
Stuckenschmidt, Heiner
Google Scholar:
Özdemir, Konrad
;
Kirchdorfer, Lukas
;
Amiri Elyasi, Keyvan
;
van der Aa, Han
;
Stuckenschmidt, Heiner
ORCID:
Özdemir, Konrad ; Kirchdorfer, Lukas ; Amiri Elyasi, Keyvan ; van der Aa, Han ; Stuckenschmidt, Heiner  ORCID: 0000-0002-0209-3859
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