Jervis Technologies
Siemens has been at the forefront of integrating artificial intelligence (AI) into manufacturing processes to enhance quality control, defect detection, and overall production efficiency. Below are notable case studies illustrating Siemens' AI-driven innovations in manufacturing, along with their respective years
AI-Based Visual Quality Inspection (2024)
In 2024, Siemens implemented AI-based models to automate visual inspections across various industries. These models significantly improved the detection of defects, including those not previously defined, enhancing flexibility and efficiency in production processes.
Siemens Electronics Factory Erlangen (2024)
At the Siemens Electronics Factory in Erlangen, AI technologies were employed to optimize products and production, improve working conditions, and enhance sustainability. The integration of AI led to increased production quality and efficiency.
Global AI Manufacturing Technologies R&D Center in Canada (2025)
In 2025, Siemens announced a CAD$150 million investment over five years to establish a Global AI Manufacturing Technologies Research and Development Center in Canada. The center focuses on developing AI technologies for battery and electric vehicle production, aiming to improve quality, reduce waste, and enhance recycling processes.
AI in PCB Production Testing (2020)
Siemens applied AI to the quality inspection of printed circuit boards (PCBs) in its manufacturing plants. The goal was to transition from a reactive, time-consuming procedure to a proactive, automated model using data analytics, resulting in lower production costs and improved efficiency.
Defective Parts Detection in Electronics Manufacturing (2021)
Siemens partnered with Cybord to create a traceability solution that enables visual inspection of every electronic component. The AI models verify component authenticity and identify damage or tampering, ensuring quality control across 100% of the components used.
AI-Based Inspection of Wind Turbine Blades (2023)
Siemens Gamesa Renewable Energy implemented AI to analyze images from non-destructive testing data scans, identifying patterns indicating manufacturing defects in wind turbine blades. This approach reduced inspection time by 75%, enhancing efficiency in quality control.
