@inproceedings{DoblerBueselHartmannetal.2020,
  author    = {Martin Dobler and Philipp B{\"u}sel and Christian Hartmann and Jens Schumacher},
  title     = {Supporting SMEs in the Lake Constance region in the implementation of cyber-physical-systems},
  series = {2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)},
  publisher = {IEEE},
  address   = {Piscataway, NJ},
  isbn      = {978-1-7281-7037-4},
  doi       = {10.1109/ICE/ITMC49519.2020.9198430},
  year      = {2020},
  abstract  = {With the emergence of the recent Industry 4.0 movement, data integration is now also being driven along the production line, made possible primarily by the use of established concepts of intelligent supply chains, such as the digital avatars. Digital avatars – sometimes also called Digital Twins or more broadly Cyber-Physical Systems (CPS) – are already successfully used in holistic systems for intelligent transport ecosystems, similar to the use of Big Data and artificial intelligence technologies interwoven with modern production and supply chains. The goal of this paper is to describe how data from interwoven, autonomous and intelligent supply chains can be integrated into the diverse data ecosystems of the Industry 4.0, influenced by a multitude of data exchange formats and varied data schemas. In this paper, we describe how a framework for supporting SMEs was established in the Lake Constance region and describe a demonstrator sprung from the framework. The demonstrator project’s goal is to exhibit and compare two different approaches towards optimisation of manufacturing lines. The first approach is based upon static optimisation of production demand, i.e. exact or heuristic algorithms are used to plan and optimise the assignment of orders to individual machines. In the second scenario, we use real-time situational awareness – implemented as digital avatar – to assign local intelligence to jobs and raw materials in order to compare the results to the traditional planning methods of scenario one. The results are generated using event-discrete simulation and are compared to common (heuristic) job scheduling algorithms.},
  language  = {en}
}