Using Physical Factory Simulation Models for Business Process Management Research. Malburg, L., Seiger, R., Bergmann, R., & Weber, B. In del-Río-Ortega , A., Leopold, H., & Santoro, F. M., editors, Business Process Management Workshops - BPM 2020 International Workshops, Sevilla, Spain, September 13 - 18, 2020, volume 397, of Lecture Notes in Business Information Processing, pages 95–107, 2020. Springer.. The original publication is available at www.springerlink.com
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Paper doi abstract bibtex 33 downloads
Paper doi abstract bibtex 33 downloads The production and manufacturing industries are currently transitioning towards more autonomous and 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.
@inproceedings{malburg_BPMResearch_2020,
title = {{Using Physical Factory Simulation Models for Business Process Management Research}},
author = {Lukas Malburg and Ronny Seiger and Ralph Bergmann and Barbara Weber},
year = 2020,
booktitle = {Business Process Management Workshops - {BPM} 2020 International Workshops, Sevilla, Spain, September 13 - 18, 2020},
publisher = {Springer.},
series = {Lecture Notes in Business Information Processing},
volume = 397,
pages = {95--107},
doi = {10.1007/978-3-030-66498-5\_8},
url = {https://doi.org/10.1007/978-3-030-66498-5\_8},
url = {http://www.wi2.uni-trier.de/shared/publications/2020_MalburgEtAl_BPM.pdf},
note = {The original publication is available at www.springerlink.com},
editor = {Adela del-Río-Ortega and Henrik Leopold and Flavia M. Santoro},
keywords = {{Cyber-Physical Production Systems, Factory Simulation Models, Business Process Management, Industry 4.0, Digital Twins}},
abstract = {The production and manufacturing industries are currently transitioning towards more autonomous and 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.}
}Downloads: 33
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