From Loss of Interest to Denial: A Study on the Terminators of Process Mining Initiatives
Publication date
2024-06-03
Editors
Guizzardi, Giancarlo
Santoro, Flavia
Mouratidis, Haralambos
Soffer, Pnina
Advisors
Supervisors
Document Type
Part of book
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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.
Keywords
Insights to action, Obstacles, Process improvement, Process mining, Taverne
Citation
Stein Dani, V, Leopold, H, van der Werf, J M E M, Beerepoot, I & Reijers, H 2024, From Loss of Interest to Denial : A Study on the Terminators of Process Mining Initiatives. in G Guizzardi, F Santoro, H Mouratidis & P Soffer (eds), Advanced Information Systems Engineering - 36th International Conference, CAiSE 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14663 LNCS, Springer, pp. 371-386. https://doi.org/10.1007/978-3-031-61057-8_22