Abstract
A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business people, facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real-life purchase order process data.
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Acknowledgements
We thank QPR Software Plc for the practical experiences from a wide variety of customer cases and for funding our research. The algorithms presented in this paper have been implemented in a commercial process mining tool QPR ProcessAnalyzer.
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Lehto, T., Hinkka, M. (2020). Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_18
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