Abstract
Existing process mining methods are primarily designed for processes that have reached a high degree of digitalization and standardization. In contrast, the literature has only begun to discuss how process mining can be applied to knowledge-intensive processes—such as product innovation processes—that involve creative activities, require organizational flexibility, depend on single actors’ decision autonomy, and target process-external goals such as customer satisfaction. Due to these differences, existing Process Mining methods cannot be applied out-of-the-box to analyze knowledge-intensive processes. In this paper, we employ Action Design Research (ADR) to design and evaluate a process mining approach for knowledge-intensive processes. More specifically, we draw on the two processes of product innovation and engineer-to-order in manufacturing contexts. We collected data from 27 interviews and conducted 49 workshops to evaluate our IT artifact at different stages in the ADR process. From a theoretical perspective, we contribute five design principles and a conceptual artifact that prescribe how process mining ought to be designed for knowledge-intensive processes in manufacturing. From a managerial perspective, we demonstrate how enacting these principles enables their application in practice.
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Acknowledgements
This research and development project is funded by the Ministry of Economic Affairs, Innovation, Digitalization, and Energy of the State of North Rhine-Westphalia (MWIDE) as part of the Leading-Edge Cluster, Intelligente Technische Systeme OstWestfalenLippe (it’s OWL) and supervised by the project administration in Jülich (PtJ). The responsibility for the content of this publication lies with the authors.
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Löhr, B., Brennig, K., Bartelheimer, C., Beverungen, D., Müller, O. (2022). Process Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. Lecture Notes in Computer Science, vol 13420. Springer, Cham. https://doi.org/10.1007/978-3-031-16103-2_18
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