Towards automated business process redesign in runtime using generative machine learning

Harl M, Zilker S, Weinzierl S (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Pages Range: 1-8

Conference Proceedings Title: Proceedings of the 32nd European Conference on Information Systems

Event location: Paphos, Cyprus CY

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. 

Authors with CRIS profile

Maximilian Harl Lehrstuhl für Digital Industrial Service Systems Sandra Zilker Lehrstuhl für Digital Industrial Service Systems Sven Weinzierl Lehrstuhl für Digital Industrial Service Systems

How to cite

APA:

Harl, M., Zilker, S., & Weinzierl, S. (2024). Towards automated business process redesign in runtime using generative machine learning. In Proceedings of the 32nd European Conference on Information Systems (pp. 1-8). Paphos, Cyprus, CY.

MLA:

Harl, Maximilian, Sandra Zilker, and Sven Weinzierl. "Towards automated business process redesign in runtime using generative machine learning." Proceedings of the European Conference on Information Systems, Paphos, Cyprus 2024. 1-8.

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