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In: A. Holzinger P. Kieseberg, A M. Tjoa, E. Weippl (Eds.): Machine Learning and Knowledge Extraction. Proceedings of the International Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE’17), LNCS 10410. Springer, Heidelberg, Germa, Springer

Towards a Framework for Assistance Systems to Support Work Processes in Smart Factories

Sebastian Robert , Sebastian Büttner , Henrik Mucha , Michael Fellmann und Carsten Röcker,
Aug 2017

Increasingly, production processes are enabled and controlled by Information Technology (IT), a development being also referred to as “Industry 4.0”. IT thereby contributes to flexible and adaptive production processes, and in this sense factories become “smart factories”. In line with this, IT also more and more supports human workers via various assistance systems. This support aims to both support workers to better execute their tasks and to reduce the effort and time required when working. However, due to the large spectrum of assistance systems, it is hard to acquire an overview and to select an adequate system for a smart factory based on meaningful criteria. We therefore synthesize a set of comparison criteria into a consistent framework and demonstrate the application of our framework by classifying three examples.

Literatur Beschaffung: A. Holzinger P. Kieseberg, A M. Tjoa, E. Weippl (Eds.): Machine Learning and Knowledge Extraction. Proceedings of the International Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE’17), LNCS 10410. Springer, Heidelberg, Germa, Springer
@inproceedings{67,
author= {Robert, Sebastian and Büttner, Sebastian and Mucha, Henrik and Fellmann, Michael and Röcker, Carsten},
title= {Towards a Framework for Assistance Systems to Support Work Processes in Smart Factories},
abstract= {Increasingly, production processes are enabled and controlled by Information Technology (IT), a development being also referred to as “Industry 4.0”. IT thereby contributes to flexible and adaptive production processes, and in this sense factories become “smart factories”. In line with this, IT also more and more supports human workers via various assistance systems. This support aims to both support workers to better execute their tasks and to reduce the effort and time required when working. However, due to the large spectrum of assistance systems, it is hard to acquire an overview and to select an adequate system for a smart factory based on meaningful criteria. We therefore synthesize a set of comparison criteria into a consistent framework and demonstrate the application of our framework by classifying three examples.},
booktitle= {A. Holzinger P. Kieseberg, A M. Tjoa, E. Weippl (Eds.): Machine Learning and Knowledge Extraction. Proceedings of the International Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE’17), LNCS 10410. Springer, Heidelberg, Germa},
year= {2017},
month= {Aug},
publisher= {Springer},
address= {Reggio Calabria, Italien},
editor= {},
pages= {59},
organisation= {},
}