Using Physical Factory Simulation Models for Business Process Management Research
Type
conference contribution
Date Issued
2020
Author(s)
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.
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
None
Book title
BPM 2020 Workshops
Publisher
Springer Nature
Volume
LNBIP
Number
397
Start page
95
End page
107
Pages
13
Official URL
Subject(s)
Eprints ID
261787
File(s)![Thumbnail Image]()
Loading...
open.access
Name
2020_MalburgEtAl_BPM.pdf
Size
9.87 MB
Format
Adobe PDF
Checksum (MD5)
144abd29c464dc4c44ae69298fe76c39