IoT-based monitoring of environmental conditions to improve the production performance

Oliver Mörth*, Matthias Josef Eder, Lukas Holzegger, Christian Ramsauer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


In order to ensure long-term competitiveness of a company, an appropriate performance measurement is essential. While the introduction of KPIs focusing on the most important information represents an effective way to monitor and evaluate performance, KPIs do not directly provide reasons behind the current situation. As the strong effects of the environmental conditions in the production area on the human performance has already been proven, their incorporation is important for further production system’s optimization. However, the basis for the required decisions builds the proper providing of relevant information. IoT application are considered as one solution for realizing an efficient and effective monitoring. Therefore, this paper first presents a concept for IoT-based monitoring of environmental conditions in the production area. Fulfilling the defined constraints scalability, adaptability and cost-effectiveness, a corresponding demonstrator has been developed and implemented in the LEAD Factory at Graz University of Technology. The demonstrator successfully enables real-time monitoring of the environmental conditions.
Original languageEnglish
Title of host publication Learning Factories across the value chain – from innovation to service – 10th Conference on Learning Factories 2020
PublisherElsevier B.V.
Number of pages6
Publication statusPublished - Apr 2020
Event10th Conference on Learning Factories: CLF 2020 - TU Graz, Virtuell, Austria
Duration: 15 Apr 202017 Apr 2020

Publication series

NameProcedia Manufacturing
PublisherElsevier B.V.


Conference10th Conference on Learning Factories
Abbreviated titleCLF 2020
Internet address


  • Environmental Conditions
  • Internet of Things
  • Monitoring

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering


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