Predictive Maintenance · Real-time Analytics
Siemens needed to help industrial teams overcome fragmented engineering tools, labor shortages, and slow design and maintenance workflows so they could design, simulate, and operate complex products and factories faster and with less cost and risk.
Reported impact
Siemens reports a 25% reduction in reactive maintenance time for workers using the Industrial Copilot, early pilots with 30% productivity improvement and potential for 50%, and customer deployments that surface up to 90% of issues before physical changes while improving throughput and lowering capex.
Siemens integrated NVIDIA technologies across its industrial software and copilots so engineering, factory, and robotics users can work from one AI-enabled digital foundation. Teamcenter, Simcenter STAR-CCM+, and the Siemens Industrial Copilot use NVIDIA Omniverse libraries and CUDA for visualization, simulation, and digital twins, while NIM and NeMo microservices supply inference and agent capabilities. DGX B200 and H200 systems provide the training and serving horsepower behind industrial AI, and Jetson Thor, Isaac Sim, and Isaac Lab support robotics and physical-AI workflows. This stack lets Siemens simulate products, plants, and factories before they are built, ground copilots in operational context, and close the loop between visualization, prediction, and action. As a result, Siemens can reduce reactive maintenance work, speed design reviews, and support customers using digital twins and industrial AI to find issues earlier and operate assets more efficiently.
Product and plant data is managed in Siemens engineering systems, simulated with GPU-accelerated tools, exposed through Omniverse-powered digital twins, and then consumed by copilots and robotics agents that return guidance, maintenance insights, and factory-optimization actions.
25% reduction in reactive maintenance time for workers using the Industrial Copilot.
Siemens is integrating NVIDIA technologies across offerings, including Siemens Teamcenter, Simcenter STAR-CCM+, and the Siemens Industrial Copilot.
The company relies on NVIDIA Omniverse libraries and NVIDIA CUDA for simulation and digital twins, NVIDIA NIM and NVIDIA NeMo microservices for AI inference
identify up to 90% of potential issues before any physical modifications are made.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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