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Contributions to Proceedings:

A. Redlein, L. Grasl:
"Impact of Emerging Technologies on Facility Services - A Mixed-Methodic Approach on Smart Building Technologies";
in: "IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society", IEEE, 2018, ISBN: 978-1-5090-6684-1, 807 - 812.



English abstract:
The Facility Service (FS) industry is the third
largest sector in the EU. In Europe, as well as in the US, around
10% of all employees work in this sector. Macro economic studies
estimate that in general 47% of all jobs will be automated due to
digitalization. FS will be highly affected by the megatrend
digitalization as numerous routine tasks are performed in this
sector. However, current studies only provide a macroeconomic
view on the changes. Therefore, this paper aims to answer the
following research questions: Which smart building technologies
are relevant for FS? Which are already in use and which services
will be affected?
The basis for the current research was a qualitative prestudy. Fifty German speaking Facility Managers were asked
about the technical and economic feasibility of smart building
technologies in the FS sector. Based on the smart building
technologies identified as relevant, the authors carried out a
quantitative literature review. This analysis did not only include
publications about the usage of smart building technologies in the
FS industry, but also considered use cases on the technologies
identified by the pre-study in other industries. In total, more than
350 cases were analyzed. Based on that literature review, a
research database was set up to explore the relevant technologies
and the affected services in detail. The next research step was to
compare the results of scientific publication with those of
commercial studies and the results of the pre-study.
Thus, this paper outlines the relevant technologies and FS. It
further presents the detailed results of the validation process

Keywords:
evaluation of smart building technologies, internet of things, artificial intelligence, data mining and machine learning, augmented and virtual reality in buildings


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/IECON.2018.8591654

Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_303611.pdf


Created from the Publication Database of the Vienna University of Technology.