Paper Number
1318
Paper Type
Short Paper
Abstract
In business process management, business process redesign (BPR) aims to improve business processes. In the past, BPR was mainly a manual task, with little computational power and typically high labor and time intensity. The increasing amount of stored process data and great advancements in generative machine learning (GML) and other analytical approaches have paved the way for automated BPR. However, existing BPR approaches are designed for offline applications and therefore restricted to computing historical data samples of business processes. In this paper, we argue performing BPR in runtime and leveraging prediction capabilities via GML achieves a higher degree of BPR automation, allowing organizations to improve their processes proactively. Accordingly, this research-in-progress paper outlines a design-science research process for designing a GML-based technique for automated BPR in runtime. In our preliminary evaluation, we present promising results for the proposed technique’s first online task, namely process model prediction, based on real-life event data.
Recommended Citation
Harl, Maximilian Victor; Zilker, Sandra; and Weinzierl, Sven, "Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning" (2024). ECIS 2024 Proceedings. 7.
https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/7
Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning
In business process management, business process redesign (BPR) aims to improve business processes. In the past, BPR was mainly a manual task, with little computational power and typically high labor and time intensity. The increasing amount of stored process data and great advancements in generative machine learning (GML) and other analytical approaches have paved the way for automated BPR. However, existing BPR approaches are designed for offline applications and therefore restricted to computing historical data samples of business processes. In this paper, we argue performing BPR in runtime and leveraging prediction capabilities via GML achieves a higher degree of BPR automation, allowing organizations to improve their processes proactively. Accordingly, this research-in-progress paper outlines a design-science research process for designing a GML-based technique for automated BPR in runtime. In our preliminary evaluation, we present promising results for the proposed technique’s first online task, namely process model prediction, based on real-life event data.
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