Data acquisition to enable Research, Education and Training in Learning Factories and Makerspaces

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

Abstract

Learning Factories have provided new ways of education and training for the future workforce and furthermore opened new
research fields. The Institute of Innovation and Industrial Management has opened its Learning Factory - the LEAD Factory - in
the year 2014, with a focus on lean, energy efficiency, agility and digitalization. With the opening of an academic Makerspace -
the Schumpeter Laboratory for Innovation - in spring 2019, there is new potential for not only looking at production topics but
extending that to earlier stages in the product life cycle like the product development. To realize the full potential of both facilities
it is vital to acquire meaningful data, analyze it and take actions accordingly. While the LEAD Factory has developed several ways
of data acquisition throughout the past years, the Schumpeter Laboratory for Innovation is at the very beginning of this process.
The aim of this paper is (1) the outlining of differences of the existing LEAD Factory and the new Schumpeter Laboratory for
Innovation regarding research, education and training, (2) the different purposes of use for the acquired data and (3) a data
acquisition concept in an academic Makerspace on the example of the Schumpeter Laboratory for Innovation including suitable
technology.
Original languageEnglish
Title of host publicationProcedia Manufacturing
Subtitle of host publication10th Conference on Learning Factories, CLF2020
Number of pages6
Publication statusAccepted/In press - 11 Feb 2020

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Keywords

  • data acquisition
  • RFID
  • wireless technology
  • Learning Factories
  • Makerspaces

Cite this

Herstätter, P., Wildbolz, T., Hulla, M. U., & Ramsauer, C. (Accepted/In press). Data acquisition to enable Research, Education and Training in Learning Factories and Makerspaces. In Procedia Manufacturing: 10th Conference on Learning Factories, CLF2020