AI Reshaping Skills: Exploring New Directions for Talent Cultivation in Taiwan’s Machine Tool Industry through German Experiences
Global Trends in AI-Driven Manufacturing Skill Demands
AI is transforming global work patterns. Research by the International Monetary Fund (IMF) in early 2024 indicated that nearly 40% of jobs worldwide will be impacted by AI. For the manufacturing sector, how exactly will AI implementation change the nature of work? The research advisory board and think tank of Germany’s "Plattform Industrie 4.0" found through empirical studies that AI's impact on manufacturing jobs varies by position requirements and corporate implementation methods; thus, there is no one-size-fits-all answer. However, it is certain that the future will be dominated by a "human-machine collaboration" model. Enterprises must plan exclusive AI strategies while providing digital skill training to employees to achieve a win-win for both the organization and its talent (acatech, 2024).
The machine tool industry is considered Taiwan’s "industrial lifeline," providing growth momentum for the overall manufacturing sector and serving as a critical global supplier. Facing the AI megatrend, redesigning talent cultivation models to meet the challenges of the smart manufacturing era has become a key industrial issue. The following two German case studies demonstrate how enterprises can collaborate with educational institutions through different models to cultivate a new generation of technical talent, providing a reference for Taiwan’s developmental direction.
German-Polish ViVA 4.0: A Cross-Border Dual System¹ Vocational Training Model
Implemented between 2018 and 2021, ViVA 4.0 was an innovative pilot project under the EU cooperation framework, covering Brandenburg, Germany, and the Lubusz Voivodeship, Poland. With a total budget exceeding €1 million—approximately €855,000 of which was supported by the European Regional Development Fund (ERDF)—the project aimed to develop and implement a joint transnational training model through knowledge transfer to enhance the competitiveness of SMEs in both countries and strengthen cross-border skills and lifelong learning.
The program adopted a bilateral parallel approach: students studied theoretical courses at vocational schools in their respective countries and underwent practical training at local enterprises, while also having the option to participate in cross-border internships. This arrangement allowed participants to acquire both local professional expertise and cross-cultural working capabilities, meeting the needs of globalized manufacturing talent.
Complementary Strategies and Curriculum Design
The project originated from the concept of complementary strengths: Germany possesses a well-established vocational education system and advanced Industry 4.0 technologies but faces a shortage of young labor; Poland has an abundant young workforce with a high willingness to learn but room for growth in advanced manufacturing technology. ViVA 4.0 recruited 15 participating companies each from Germany and Poland, focusing on metal and electrical engineering.
Taking into account the different training programs and frameworks of both countries, they jointly developed 10 general training modules combining theory and practice:
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Germany: Focused on automation control technology, Programmable Logic Controllers (PLC), AutoCAD, E-Plan planning software, and automation technology.
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Poland: Specialized in CNC metal processing, non-metal processing, tool measurement, CAD-CAM integration, and material testing technology.
All modules were designed to be bilingual, requiring students to master professional terminology in a foreign language, thereby enhancing their linguistic and international communication skills.
Success Factors and Features
The key to ViVA 4.0’s success lay in the deep involvement of enterprises, with industrial organizations acting as bridges. On the German side, the regional QCW Industrial Training Center was responsible, while the Polish side was led by the local metal cluster association, LKM. This ensured that training content met market demands and encouraged close cooperation between companies and educational institutions in curriculum design, internship provision, and skill certification. Furthermore, upon completion, students received the EUROPASS certification, widely recognized across Europe. ViVA 4.0 also established a sustainable resource-sharing mechanism, where all training equipment and materials remained free to use after the project ended, ensuring long-term investment benefits.
The INex-ÜBA Project: National-Level Skill Reskilling
In 2023, the German government launched the "Excellence in Inter-company Training" (INex-ÜBA) project. The core objective is to support inter-company training centers (ÜBA) to evolve into excellent third-party vocational training venues alongside schools and enterprises. By utilizing forward-looking technologies and innovative methods—such as digital tech and AI—the project systematically improves the organization and execution of inter-company training to ensure the cultivation of skilled talent and support Germany's industrial transformation.
NextGenLearn: Addressing AI Skill Needs in the Machine Tool Industry
Following a competitive bidding process, 17 projects utilizing digital technology and AI across various industries were selected for funding, with launches starting in 2025. Among them, the sub-project NextGenLearn specifically develops smart training systems for the mechanical processing field, directly responding to the urgent demand for AI-skilled talent in the German machine tool industry.
The project’s philosophy is that since AI has become a core technology of modern manufacturing, vocational education must cultivate AI competency as a foundational skill rather than an advanced elective. By installing AI learning analysis systems on network-connected lathes and milling machines, technicians can interact with AI from basic operations onward, gradually establishing a human-machine collaborative workflow.
Personalized Learning on Smart Machine Tools
The project integrates AI learning analysis systems directly into traditional machine tool equipment. These smart tools can monitor student operations in real-time, analyze skill development, and provide tailored training recommendations based on individual progress. For example, during a turning operation, the AI system can simultaneously analyze tool selection, cutting parameters, and toolpath planning, providing immediate feedback. As the student progresses, the AI increases the difficulty level, guiding them toward more complex processing techniques. This personalized model transforms standardized teaching into a pace-and-ability-based approach.
Institutional Innovation in Inter-company Training
Another feature of NextGenLearn is the establishment of an inter-company smart training network. Participating companies can benefit from shared experience and knowledge. Driven by the national INex-ÜBA initiative, this not only provides funding but also establishes national vocational standards for AI education, ensuring consistent AI application capabilities among technical talent across different regions and companies.
New Directions for Taiwan’s Machine Tool Industry
These cases illustrate how Germany, as a manufacturing powerhouse, systematically addresses talent challenges. ViVA 4.0 demonstrates the potential of transnational cooperation, while INex-ÜBA opens an innovative model for inter-company training. For Taiwan, the key is not to replicate the German model exactly, but to learn from its institutional innovation logic to develop a localized smart manufacturing talent ecosystem.
To move forward, Taiwan must address several practical challenges:
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From Passive to Active Participation: Inspired by ViVA 4.0, Taiwan can deepen industry-academic cooperation. However, since many Taiwanese machine tool manufacturers focus on OEM/ODM, the industry must evaluate whether companies are willing to assume deeper training responsibilities and how to overcome resource constraints.
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Modular Curriculum Design: Industry and academia should jointly develop modular courses that blend digital technology with traditional craftsmanship. Unlike Germany’s unified standards, Taiwan requires more refined institutional design to bridge the gap between vocational systems and industrial needs.
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Leveraging Industrial Clusters: By utilizing industry associations to integrate resources from industry, government, academia, and research, the costs for individual enterprises can be reduced. Establishing effective coordination mechanisms will require stronger incentives and intermediary bridges.
Finally, as Taiwan’s machine tool industry is export-oriented with production bases already established in Southeast Asia, the industry has the foundation to pursue transnational talent cultivation. Referencing ViVA 4.0’s emphasis on both language and technology, Taiwan could establish bilateral training exchange mechanisms with Southeast Asian countries. This would not only address the domestic shortage of skilled labor but also cultivate international talent familiar with local markets, strengthening the global competitive advantage of Taiwan’s machine tool industry.
{1} Dual System (Duales System): A vocational education model supported by national legislation where vocational schools provide theoretical knowledge and enterprises provide practical skill training, forming a "school-enterprise" dual learning mode.
Reference
- Cazzaniga et al. (2024). “Gen-AI: Artificial Intelligence and the Future of Work.” IMF Staff Discussion Note SDN2024/001, International Monetary Fund, Washington, DC.
- Acatech （2024）. Künstliche Intelligenz und industrielle Arbeit –Perspektiven und Gestaltungsoptionen. https://www.acatech.de/publikation/ki-industrielle-arbeit/。
- https://interregva-bb-pl.eu/viva/
- https://qcw.de/ViVA/Module/Ausbildungsmodell-ViVA40.pdf
- https://www.bmftr.bund.de/SharedDocs/Bekanntmachungen/DE/2023/07/2023-07-27-Bekanntmachung-INex-%c3%9cBA.html
- https://www.bibb.de/de/204344.php
