<div class="csl-bib-body">
<div class="csl-entry">Scheuvens, M., Mozer, P., Fuchs, B., Dworschak, B., Ansari, F., & Hölzle, K. (2024). DaWiK – Ein KI-gestützter Ansatz für digitales Wissens- und Kompetenzmanagement. <i>ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb</i>, <i>119</i>(11), 788–793. https://doi.org/10.1515/zwf-2024-1158</div>
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dc.identifier.issn
0947-0085
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/208853
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dc.description.abstract
DaWiK – An AI-supported Approach for Digital Knowledge and Competence Management – A Case Study from the Automotive Supply Industry. Effective competency management is increasingly vital for companies facing demographic changes and higher turnover, as it enables optimal talent utilization and development, enhancing productivity and work quality. Semantic technologies allow precise modeling and identification of competency gaps, supporting targeted development measures. The DaWiK project has developed a data-driven approach, using the Semantic Text Analysis Pipeline (STAP) to systematically capture requirements from unstructured data like job postings, enabling companies to steer competency development with precision and a future-oriented focus.
en
dc.language.iso
de
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dc.publisher
De Gruyter
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dc.relation.ispartof
ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb
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dc.subject
Competency Management
en
dc.subject
Data-Driven
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dc.subject
Framework
en
dc.subject
Knowledge Management
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dc.subject
Semantic Technologies
en
dc.title
DaWiK – Ein KI-gestützter Ansatz für digitales Wissens- und Kompetenzmanagement
