Investigating the Influence of Data-Aware Process States on Activity Probabilities in Simulation Models: Does Accuracy Improve?

Massimiliano de Leoni, Francesco Vinci, Sander J. J. Leemans, Felix Mannhardt

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Business process simulation enables analysts to run a process in different scenarios, compare its performances and consequently provide indications on how to improve a business process. Process simulation requires one to provide a simulation model, which should accurately reflect reality to ensure the reliability of the simulation findings. An accurate simulation model passes through a correct stochastic modelling of the activity firings: activities are associated with the probability of each to fire. Literature determines these probabilities by looking at the frequency of the activity occurrences when they are enabled. This is a coarse determination, because this way does not consider the actual process state, which might influence the probabilities themselves (e.g., a thorough loan assessment is more likely for larger loan requests). The process state is as a faithful abstraction of the process instance execution so far, including the process-variable values, the activity firing history, etc. This paper aims to investigate how process states can be leveraged to improve activity firing probabilities. A technique has been put forward and compared with the baseline where basic branching probabilities are employed. Experimental results show that, indeed, business simulation models are more accurate to replicate the real process’ behavior.

Original languageEnglish
Title of host publicationBusiness Process Management - 21st International Conference, BPM 2023, Proceedings
EditorsChiara Di Francescomarino, Andrea Burattin, Christian Janiesch, Shazia Sadiq
PublisherSpringer
Pages129-145
Number of pages17
ISBN (Electronic)978-3-031-41620-0
ISBN (Print)9783031416194
DOIs
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14159 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

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Funding

Acknowledgements. This work is supported by Digital Research Centre Denmark (DIREC). We acknowledge Søren Debois for sharing his Isabelle/HOL DCR Graph formalization. Acknowledgements. Special thanks to our advisors Xixi Lu, Niels Martin, Vinicius Stein Dani, and Lisa Zimmermann, who have helped with their research to fill some of the gaps in supporting process analyst. F. Zerbato and B. Weber are supported by the ProMiSE project funded by the SNSF under Grant No.: 200021_197032} The research by Jan Mendling was supported by the Einstein Foundation Berlin under grant EPP-2019-524 and by the German Federal Ministry of Education and Research under grant 16DII133. The research is financially supported by MUR (PNRR) and University of Padua, by the Department of Mathematics of University of Padua, through the BIRD project “Data-driven Business Process Improvement” (code BIRD215924/21), and by the “Smart Journey Mining project” funded by the Research Council of Norway (grant no. 312198). Acknowledgement. This research was supported by the Flemish Fund for Scientific Research (FWO) with grant number G0B6922N. – Channel Networks: In channel networks, multiple channels are supported by one root contract. In our current design, the channel smart contract is appli-cation specific. Exploring a design where a contract can support multiple processes could pave the way toward a network of cost efficient, blockchain-based choreographies. Acknowledgements. This work has been funded by the European Research Council (PIX Project) and the National Science and Engineering Research Council (NSERC) grants held by Opher Baron, Dmitry Krass, and Arik Senderovich. Supported by the EPSRC Prosperity Partnership FAIR (grant number EP/V056883/1). MK receives funding from the ERC under the European Union’s Horizon 2020 research and innovation programme (FUN2MODEL, grant agreement No. 834115). Acknowledgments. Andrei Tour was supported via an “Australian Government Research Training Program Scholarship.” Artem Polyvyanyy was in part supported by the Australian Research Council project DP220101516. First author supported by the Karlsruhe House of Young Scientists Research Travel Grant. Second author supported by the International Postdoctoral Fellowship (IPF) Grant (Number: 1031574) from the University of St. Gallen, Switzerland. Acknowledgement. The research that led to this publication was partly funded by the European Supply Chain Forum (ESCF) and the Eindhoven Artificial Intelligence Systems Institute (EAISI) under the AI Planners of the Future program. Jan Mendling: The research by Jan Mendling was supported by the Einstein Foundation Berlin under grant EPP-2019-524 and by the German Federal Ministry of Education and Research under grant 16DII133. Acknowledgements. This work has been partly funded by SAP SE in the context of the research project “Building Semantic Models for the Process Mining Pipeline” and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 277991500. This work has been partially supported by projects PID2021-126227NB-C21/ AEI/10.13039/501100011033/ FEDER, UE; TED2021-131023B-C22/ AEI/10.13039/501100011033/ Unión Europea NextGenerationEU/PRTR, and US-1381595 (Junta de Andalucía/FEDER, UE).

FundersFunder number
Building Semantic Models for the Process Mining Pipeline
University of PadovaBIRD215924/21
Digital Research Centre Denmark
EPSRC Prosperity Partnership FAIREP/V056883/1
Eindhoven University of Technology
European Supply Chain Forum
Flemish Fund for Scientific Research
PRTRUS-1381595
SAP
Unión Europea NextGenerationEU
Universität St. Gallen
European Union's Horizon 2020 - Research and Innovation Framework Programme834115
Natural Sciences and Engineering Research Council of Canada
European Research Council
Australian Research CouncilDP220101516
Deutsche Forschungsgemeinschaft277991500
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung200021_197032
Bundesministerium für Bildung und Forschung16DII133
Fonds Wetenschappelijk OnderzoekG0B6922N
Italian Ministero dell’ Istruzione, Università e Ricerca
University of Padova
Norges Forskningsråd312198
Einstein Stiftung BerlinEPP-2019-524
European Regional Development FundTED2021-131023B-C22/ AEI/10.13039/501100011033
Masarykova UniverzitaMUNI/A/1315/2022
Junta de Andalućıa

    Keywords

    • Branching Probabilities
    • Process Mining
    • Process Simulation
    • Stochastic Models

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