Repository logo
  • English
  • Deutsch
Log In
or
  1. Home
  2. HSG CRIS
  3. HSG Publications
  4. Using Physical Factory Simulation Models for Business Process Management Research
 
  • Details

Using Physical Factory Simulation Models for Business Process Management Research

Type
conference contribution
Date Issued
2020
Author(s)
Malburg, Lukas
Seiger, Ronny  
Bergmann, Ralph
Weber, Barbara  
DOI
10.1007/978-3-030-66498-5_8
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.
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
https://doi.org/10.1007/978-3-030-66498-5_8
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/112953
Subject(s)

computer science

Division(s)

ICS - Institute of Co...

SCS - School of Compu...

Eprints ID
261787
File(s)
Loading...
Thumbnail Image

open.access

Name

2020_MalburgEtAl_BPM.pdf

Size

9.87 MB

Format

Adobe PDF

Checksum (MD5)

144abd29c464dc4c44ae69298fe76c39

here you can find instructions and news.

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Privacy policy
  • End User Agreement
  • Send Feedback