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Press Release19 October 2026Siemens AGMunich
Siemens partners with NVIDIA to advance AI-driven, autonomous factories
Erlangen serves as a living lab for industrial AI and a blueprint
for scaling these capabilities across industries. Siemens and NVIDIA aim to
make artificial intelligence the operating system of manufacturing,
transforming how products are designed, manufactured and delivered across the
entire value chain. Further applications are planned in the coming months.
“The
historic opportunity for Germany and Europe lies in bringing artificial
intelligence out of the data centers and into the factories – and applying it
on an industrial scale,” says Cedrik Neike, member of the Managing Board of
Siemens AG and President Digital Industries. “Today, together with NVIDIA
at our Siemens Electronic Works in Erlangen, we are showing how this can be
done. With an annual productivity increase of seven percent, Erlangen is one of
the most advanced manufacturing plants in the world. At the same time, with
around 3,000 customer visits every year, it is a global meeting place for
industrial innovation. Here, the future of industry is not just discussed; it
is becoming reality today.”
“Factories
need to handle change as well as they handle repetition. AI and simulation make
that possible,” said Rev Lebaredian, Vice President, Physical AI Simulation at
NVIDIA. “At Erlangen, Siemens and NVIDIA are putting years of collaboration to
work on the factory floor – training inspection systems before physical
prototypes exist, designing and testing production plans in simulation before
changing the real world. Together, we’re building a blueprint for autonomous
factories everywhere.”
The joint Siemens and NVIDIA team developed the first
new AI offerings in less than ten months,
combining NVIDIA’s AI,
simulation and accelerated computing
technologies with Siemens’ industrial software, automation and manufacturing expertise. Testing the solutions directly in a production
environment enabled the teams to develop solutions around real manufacturing
requirements. These new technologies can deliver unprecedented improvements in
areas such as production planning and product development.
For new printed circuit
boards (PCB) assemblies – the core of the drives and frequency converters
manufactured in Erlangen – virtual production
preparation can reduce engineering
effort by up to 85 percent and cut time-to-market by up to 40 percent.
The workflow addresses a critical bottleneck: preparing quality
inspection previously took several weeks, because teams had to wait for
physical prototypes to train vision systems and manually configure inspection equipment.
AI now generates photorealistic representations of PCB assemblies from their
digital twins to train the vision systems, enabling engineers to prepare
inspection before physical prototypes are available.
In the plant’s
production planning, new AI technologies also promise to master the balancing
act between capacity, material availability, delivery dates and production lines
much faster and more efficiently. Previously, planners had to weigh various
factors against each other based on their experience to make the best possible
decision. At the Siemens Electronics Factory in Erlangen, an AI agent now
supports this process. When new orders arrive, it automatically generates
various production scenarios and shows planners the impact of each option based
on defined KPIs. Before implementation, these proposals are validated using a Digital
Twin of the entire production facility. This end-to-end agent-based production
planning can enable cost savings of up to 20 percent, while allowing
manufacturing operations to respond more flexibly to new orders, material
shortages, or shifting customer requirements.
These
advances combine Siemens’ industrial software and manufacturing expertise with
NVIDIA technologies. NVIDIA Cosmos world models generate realistic training
data for quality inspection. The NVIDIA Factory Operations Blueprint supports
AI agents that create production plans and help factories respond to changing
production needs, while NVIDIA Omniverse libraries enable teams to test those
plans virtually using real factory data.
Established
in 1971, the Siemens Erlangen plant in Southern Germany employs approximately
1,000 people and manufactures power electronics and automation technology for
industry and infrastructure – including machine tools, battery production, as
well as the process and discrete manufacturing industries. For more than a
decade, new digital manufacturing technologies have been tested, developed and
deployed at the site during live operations. Currently, more than 100 AI
algorithms are already in active use across production, logistics, quality
inspection and maintenance. The location is set for further expansion through
2030, with Siemens investing €500 million in the transformation.
Press pictures
For this press release
Siemens AG (Berlin and Munich) is a leading technology company focused on industry, infrastructure, mobility, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. In fiscal 2025, which ended on September 30, 2025, the Siemens Group generated revenue of €78.9 billion and net income of €10.4 billion. As of September 30, 2025, the company employed around 318,000 people worldwide on the basis of continuing operations. Further information is available on the Internet at www.siemens.com.