Article (Scientific journals)
Lessons from social network analysis to Industry 4.0
Omar, Yamila; Minoufekr, Meysam; Plapper, Peter
2018In Manufacturing Letters, 15B, p. 97-100
Peer reviewed
 

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Keywords :
Complex Networks; Smart Manufacturing; Industry 4.0
Abstract :
[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.
Disciplines :
Mechanical engineering
Author, co-author :
Omar, Yamila ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Minoufekr, Meysam ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Plapper, Peter ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
External co-authors :
no
Language :
English
Title :
Lessons from social network analysis to Industry 4.0
Publication date :
January 2018
Journal title :
Manufacturing Letters
Publisher :
Elsevier
Volume :
15B
Pages :
97-100
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 15 January 2018

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