Whether mechanical engineering, medical technology, or automotive supply – industrial production is at the heart of Baden-Württemberg. But how can we make the transition from digital to intelligent? Our look at practical applications shows how AI is rethinking production – and why the transformation is already in full swing.
Production reimagined – what does that actually mean?
Production encompasses much more than just the classic image of an assembly line. Today, it stands for all processes involved in the manufacture of goods – from planning and production to logistics and quality assurance. In a federal state such as Baden-Württemberg, which is strongly industrialized, production plays a central economic role: from mechanical and plant engineering to automotive supply, medical technology, precision manufacturing, and plastics processing – the industrial diversity is vast.
And this is precisely where the potential lies: artificial intelligence can not only automate these processes, but also develop them intelligently—making them accessible, understandable, and practical.
Best practices: Successful examples from Baden-Württemberg
Balluff: How AI expands classic sensor technology
At sensor specialist Balluff in Neuhausen auf den Fildern, artificial intelligence is expanding proven sensor technology with new possibilities. For example, temperature information can be obtained directly from the raw signals of magnetostrictive sensors – without any additional hardware. The AI recognizes subtle patterns in the signal that reflect temperature-dependent behavior and uses them for precise estimation. This not only gives customers deeper insights into the condition of the system, but also enables them to detect critical situations or potential overloads at an early stage. Balluff thus seamlessly integrates modern AI methods into established sensor technologies – for greater intelligence, transparency, and safety in industrial applications.
STIHL: AI expertise becomes part of corporate culture
STIHL, based in Waiblingen, Germany, impressively demonstrates how AI is changing not only processes but also ways of thinking. Jens Klöker, a career changer and data and AI enthusiast, developed an AI-based anomaly detection system that was introduced in collaboration with domain experts from production. This enables production data to be understood more quickly, anomalies to be identified in a targeted manner, and, as a result, informed decisions to be made. The internal AI @ STIHL community he created actively shares experiences with AI and AI-specific knowledge. This creates a learning culture in which AI is demystified and subsequently understood as a common tool for optimization.
Automated inspection at PVA TePla
At PVA TePla in Westhausen, artificial intelligence is used in fully automated inspection systems – for example, to test wafers and power modules used in electric cars, wind turbines, and Industry 4.0 solutions. Integrated AI algorithms enable precise, non-destructive quality control down to the nanometer range—for example, to detect delamination of functional layers. The result: faster processes, greater safety for live components, and a clear contribution to digitalization and sustainable technology development.
Classification: From Industry 4.0 to the learning factory
With the advent of digitalization and Industry 4.0, data became the driving force in production. Machines were networked and processes became transparent—but the real transformation is only just beginning. The use of artificial intelligence is giving rise to production systems that act proactively, learn from experience, and adapt to changing requirements.
This intelligent production method offers new opportunities, particularly for small and medium-sized enterprises: they can respond more flexibly to market changes, use resources more efficiently, and make complex processes manageable. The technology does not conflict with human capabilities—it complements them and opens up new scope for quality, sustainability, and innovation.
Where AI has a particularly strong impact in production
AI technologies can be used in a targeted manner at many points along the value chain. Their potential is particularly strong in the following areas of application:
- Quality assurance: Machine learning-supported image processing allows errors to be detected in real time—more accurately and quickly than with manual inspection methods.
- Predictive maintenance: Sensors continuously collect machine data. AI analyzes this data and detects wear or anomalies at an early stage—before production downtime occurs.
- Production planning and control: Complex interdependencies can be optimized automatically. This applies, for example, to the sequence of production orders or the deployment of personnel and materials.
- Intralogistics: Intelligent localization and analysis of material flows make warehousing and internal transport more transparent and efficient.
- Energy efficiency: AI-based optimization helps reduce electricity and raw material consumption—an important step toward sustainable production.
- Assistance systems: AI supports employees in their decision-making, for example through visual cues, voice output, or digital recommendations for action.
Become part of the network now—and help shape change
Practical experience shows that the use of artificial intelligence in production is no longer a vision of the future—it is reality. However, access to suitable solutions often remains unclear, especially for small and medium-sized enterprises. The AI Alliance Baden-Württemberg is a reliable point of contact in this regard, offering an overview, guidance, and a strong network that specifically connects business, science, politics, and administration.
Does that sound interesting?
Become a member of the AI Alliance now and play an active role in shaping the future of intelligent production.