From Loss of Interest to Denial: A Study on the Terminators of Process Mining Initiatives

Vinicius Stein Dani*, Henrik Leopold, Jan Martijn E. M. van der Werf, Iris Beerepoot, Hajo Reijers

*Corresponding author for this work

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

Abstract

Process mining has been used to obtain insights into work processes in various industries. While there is plenty of evidence that process mining has helped a number of organizations to improve their processes, there are also a few studies indicating that it did not happen in other cases. An obvious yet frequently overlooked challenge in that context is that organizations actually need to take action based on the insights process mining tools and techniques provide. In practice, analysts typically use process mining insights to recommend actions, which then need to be performed and implemented, for example, by process owners or management. If, however, recommended actions are not performed, the insights will not help organizations to progress into process improvement either. Recognizing this, we use this paper to develop a better understanding of the extent to which recommended actions are actually performed, as well as the causes hampering the progress from recommended to performed actions. To this end, we combine a systematic literature review involving 57 papers with 17 semi-structured interviews of process mining experts. Based on our analysis, we discover specific causes why organizations do not perform recommended actions. These findings are crucial for both researchers and organizations to develop measures to anticipate and mitigate these causes.
Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering - 36th International Conference, CAiSE 2024, Proceedings
EditorsGiancarlo Guizzardi, Flavia Santoro, Haralambos Mouratidis, Pnina Soffer
PublisherSpringer
Pages371-386
Number of pages16
ISBN (Electronic)978-3-031-61057-8
ISBN (Print)978-3-031-61056-1
DOIs
Publication statusPublished - 3 Jun 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Funding

Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek16672
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    Keywords

    • Insights to action
    • Obstacles
    • Process improvement
    • Process mining

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