Digital twin technology offers immersive and cost-effective solutions for workforce training, yet its practical implementation within SME training frameworks remains limited. This study develops and evaluates the Digital Twin Framework for Workforce Training (DT4WFT), addressing key challenges such as resource constraints and precision through tools like Siemens NX Mechatronic Concept Design (MCD) and StoneCUT@Line^®. Employing a mixed-methods approach, qualitative insights from managers, and quantitative analysis demonstrated the framework’s potential to enhance operator performance, improve efficiency, and reduce lead times. However, the validation was based solely on managerial perceptions, as the framework has not yet been implemented in real-world settings. Statistical analysis confirmed strong correlations between the framework’s perceived implementation and improved training outcomes, highlighting its scalability and adaptability. Future research should focus on practical implementation, cross-industry applications, and longitudinal studies to evaluate sustained impacts, ensuring the DT4WFT framework’s broader relevance and effectiveness in workforce development.

The research highlights the transformative potential of digital twins in addressing workforce training gaps while emphasizing the importance of secure and reliable communication protocols. Future directions include expanding the framework to other traditional industries, incorporating adaptive artificial intelligence, and exploring long-term impacts on workforce productivity and organizational innovation. This study provides a robust foundation for integrating digital twin technology into SME training strategies, ensuring sustained competitiveness and growth in rapidly evolving markets.