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Skills Intelligence in the Steel Sector †.

Authors :
Maldonado-Mariscal, Karina
Cuypers, Mathias
Götting, Adrian
Kohlgrüber, Michael
Source :
Machines; Mar2023, Vol. 11 Issue 3, p335, 22p
Publication Year :
2023

Abstract

The ecological and digital transformations of the steel industry intensify already existing skill shortages and create specific skill demands that are currently not being met. One of the main problems in this sector lies in the lack of sufficient information on which skills companies need and which skills trainings are suitable for today's challenges. In addition, more information is needed to provide more and better information for policy-making processes for getting the sector's workforce well-equipped for digitalisation and decarbonisation. This paper uses the framework of skills intelligence in the steel sector, reflecting on theoretical developments and the application of concrete tools in the European projects BEYOND 4.0 and ESSA. The main research questions guiding this work are: To what extent is the concept of skills intelligence useful in the steel sector, and how can it be applied in the steel sector in Europe? This paper provides empirical data based on qualitative and quantitative research carried out in the mentioned projects. The main contribution of this paper is the development of concrete reflections on the concept of skills intelligence based on tools in the steel sector. This work operationalises the skills intelligence approach at sectoral level, namely for the steel industry, and shows how this sector approach can be implemented at the European, national and regional levels. The main findings suggest that skills intelligence in the steel sector is not limited to the preparation and presentation of data but creates a governance structure to mitigate skills imbalances. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20751702
Volume :
11
Issue :
3
Database :
Complementary Index
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
162807844
Full Text :
https://doi.org/10.3390/machines11030335