TY  - CONF
A1  - Frierson, Charlotte
A1  - Wrobel, Jana
A1  - Senderek, Roman
A1  - Stich, Volker
A2  - Herberger, D.
A2  - Hübner, M.
A2  - Stich, Volker
T1  - Conceptualization of an AI-based Skills Forecasting Model for Small and Medium-Sized Enterprises (SMEs)
T2  - Proceedings of the Conference on Production Systems and Logistics: CPSL 2023
N2  - Forecasting-based skills management, which is oriented to the respective corporate goals, is gaining enormous importance as a central management tool. The aim is to predict future skills requirements and match them with existing interorganizational skills. Companies are required to anticipate changes in markets, industries, and technologies at an early stage as well as to identify changes in job profiles within an occupational profile by tapping into and evaluating various data sources. Based on these findings, they can then make informed decisions regarding skill gaps, for example, to implement targeted further training measures. Forecasting-based skills management offers the opportunity to optimally qualify employees for constantly changing tasks. At the same time, however, the targeted development of such skills requires a high level of time, financial and personnel resources, which small and medium-sized enterprises (SMEs) generally do not have at their disposal. In addition, many SMEs are not yet aware of the importance of this issue. Within the framework of research and industrial projects of the Smart Work department at the FIR (Institute for Industrial Management) at the RWTH Aachen University, an AI-based skills forecasting tool will be developed. The goal of the paper is to conceptualize the future machine learning method, that is able to generate individualized skills forecasts and recommendations for SMEs. This is achieved by linking societal forecasts and sector trends with company-specific conditions and skills. In order to generate a corresponding database, the derivation system is made available to various companies (large companies and SMEs) in order to obtain as many data sets as possible. The data sets obtained via the derivation system are then used as training data sets for the machine learning method, with the help of which an automatic derivation of competencies depending on new trends is to be made possible.
KW  - Machine Learning
KW  - Skills Management
KW  - Skills Forecasting
KW  - Competencies
KW  - AI
KW  - Employee Qualification
KW  - rev
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/2526
UR  - https://www.repo.uni-hannover.de/handle/123456789/13609
SP  - 801
EP  - 811
PB  - publish-Ing.
CY  - Hannover
ER  -