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Aiming for knowledge-transfer-optimizing intelligent cyber-physical systems

  • Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding productionSince more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Marcus GrumORCiDGND, Christof ThimORCiDGND, Norbert GronauORCiDGND
DOI:https://doi.org/10.1007/978-3-030-90700-6_16
ISBN:978-3-030-90699-3
ISBN:978-3-030-90700-6
ISBN:978-3-030-90702-0
Titel des übergeordneten Werks (Englisch):Towards sustainable customization : cridging smart products and manufacturing systems
Verlag:Springer
Verlagsort:Cham
Herausgeber*in(nen):Ann-Louise Andersen, Rasmus Andersen, Thomas Ditlev Brunoe, Maria Stoettrup Schioenning Larsen, Kjeld Nielsen, Alessia Napoleone, Stefan Kjeldgaard
Publikationstyp:Teil eines Buches (Kapitel)
Sprache:Englisch
Datum der Erstveröffentlichung:01.11.2021
Erscheinungsjahr:2021
Datum der Freischaltung:20.09.2023
Freies Schlagwort / Tag:human-machine-interaction; smart automation; smart production
Seitenanzahl:9
Erste Seite:149
Letzte Seite:157
Organisationseinheiten:Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre
DDC-Klassifikation:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Peer Review:Nicht ermittelbar
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