Abstract
Manufacturing enterprises can identify organizational and human resource competence fields and individual competencies, which are documented. They mostly rely on technology management approaches to predict and supply future demanding competencies. Technology trends are examined and evaluated, and relevant competencies are derived. Others use data-driven trend analysis gaining benefits from internal and/or external data sources and expert opinions. However, a few manufacturing industries consider systematic selection and extensive use of competence-relevant sources and tools. Therefore, the planning and prognosis of future competencies remain inaccurate and incomplete. Although data-driven approaches majorly contribute to this area, the correct selection of data sources, objective assessment of causalities, and, thus, correct interpretation of findings are mostly unresolved challenges. A structured analysis of competence sources is carried out to contribute to the body of knowledge in competence management and provide a practice-oriented solution. External and internal competence sources are identified and combined in a targeted manner to compensate for the weaknesses of state-of-the-art approaches. In this paper, the initial results of the framework model are presented. A systematic literature analysis is conducted to identify possible competence sources. Target competence sources are described, and an initial classification is provided. As the main result, the classification of competence sources is transferred into a decision-support framework model for identifying future competence sources of workers in manufacturing companies. The developed framework model enables manufacturing enterprises to plan additional competence sources and relate them to job profiles on the shop floor level.
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Steinlechner, M., Ansari, F., Schlund, S. (2023). Evolution of Competence Management in Manufacturing Industries. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Rauch, E., Piteľ, J. (eds) Advances in Design, Simulation and Manufacturing VI. DSMIE 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-32767-4_6
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