Reference : Lessons from social network analysis to Industry 4.0
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/10993/34026
Lessons from social network analysis to Industry 4.0
English
Omar, Yamila mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Minoufekr, Meysam mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Plapper, Peter mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
In press
Manufacturing Letters
Elsevier
Yes
International
[en] Complex Networks ; Smart Manufacturing ; Industry 4.0
[en] With the advent of Industry 4.0, a growing number of sensors within modern production lines generate high volumes of data. This data can be used to optimize the manufacturing industry in terms of complex network topology metrics commonly used in the analysis of social and communication networks. In this work, several such metrics are presented along with their appropriate interpretation in the field of manufacturing. Furthermore, the assumptions under which such metrics are defined are assessed in order to determine their suitability. Finally, their potential application to identify performance limiting resources, allocate maintenance resources and guarantee quality assurance are discussed.
http://hdl.handle.net/10993/34026
10.1016/j.mfglet.2017.12.006

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