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.…
Author details: | 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 |
Title of parent work (English): | Towards sustainable customization : cridging smart products and manufacturing systems |
Publisher: | Springer |
Place of publishing: | Cham |
Editor(s): | Ann-Louise Andersen, Rasmus Andersen, Thomas Ditlev Brunoe, Maria Stoettrup Schioenning Larsen, Kjeld Nielsen, Alessia Napoleone, Stefan Kjeldgaard |
Publication type: | Part of a Book |
Language: | English |
Date of first publication: | 2021/11/01 |
Publication year: | 2021 |
Release date: | 2023/09/20 |
Tag: | human-machine-interaction; smart automation; smart production |
Number of pages: | 9 |
First page: | 149 |
Last Page: | 157 |
Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre |
DDC classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Peer review: | Nicht ermittelbar |