Using Physical Factory Simulation Models for Business Process Management Research

Item Type Book Section
Abstract

The production and manufacturing industries are currently transitioning towards more autonomous an
intelligent production lines within the Fourth Industrial Revolution (Industry 4.0). Learning Factories as small scale physical models of real shop floors are realistic platforms to conduct research in the smart manufacturing area without depending on expensive real world production lines or completely simulated data. In this work, we propose to use learning factories for conducting research in the context of Business Process Management (BPM) and Internet of Things (IoT) as this combination promises to be mutually beneficial for both research areas. We introduce our physical Fischertechnik factory models simulating a complex production line and three exemplary use cases of combining BPM and IoT, namely the implementation of a BPM abstraction stack on top of a learning factory, the experience-based adaptation and optimization of manufacturing processes, and the stream processing-based conformance checking of IoT-enabled processes.

Authors Malburg, Lukas; Seiger, Ronny; Bergmann, Ralph & Weber, Barbara
Language English
Subjects computer science
HSG Classification contribution to scientific community
HSG Profile Area None
Date 2020
Publisher Springer Nature
Volume LNBIP
Number 397
Page Range 95-107
Number of Pages 13
Title of Book BPM 2020 Workshops
Publisher DOI 10.1007/978-3-030-66498-5_8
Official URL https://doi.org/10.1007/978-3-030-66498-5_8
Depositing User Dr.-Ing. Ronny Seiger
Date Deposited 21 Dec 2020 15:51
Last Modified 18 Jan 2021 13:31
URI: https://www.alexandria.unisg.ch/publications/261787

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Citation

Malburg, Lukas; Seiger, Ronny; Bergmann, Ralph & Weber, Barbara: Using Physical Factory Simulation Models for Business Process Management Research. In BPM 2020 Workshops. Springer Nature, 2020, S. 95-107.

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https://www.alexandria.unisg.ch/id/eprint/261787
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