Cohort: 1 

Location: Zug, Switzerland 

Application Title: Industrial AI framework for quality control and operational excellence

Team Members

EthonAI: Julian Senoner (Executive Sponsor), Bernhard Kratzwald, Davide Vecchione, Hendrik Schmid, Arman Pour Tak Dost.

Siemens: Peter Koerte (Executive Sponsor), Sébastien Bey, Alexander Dierolf, Andreas Duerr, Matteo Odermatt, Sascha Leadbetter

The challenge 

Manufacturing relies on consistent quality to control costs and meet customer expectations. Yet even in highly automated factories, a large share of available production data is never analysed. Sensors generate continuous streams of information about machines, processes and outputs, but much of this data remains unused. 

Quality control often depends on manual inspection or narrowly scoped automation. Visual checks are set up line by line and tuned locally. When defects appear, engineers typically rely on experience-driven root cause analysis supported by small data samples. These approaches can identify obvious problems, but they struggle to keep pace with the volume and complexity of modern production data. As a result, inefficiencies persist, scrap increases and improvement efforts remain difficult to scale across sites. 

The solution 

A standardised AI platform for factory-wide quality control 

Siemens partnered with EthonAI to introduce a unified industrial AI platform for quality control and operational analysis. 

The platform is integrated with Siemens Industrial Edge, allowing it to run directly within factory environments. Rather than deploying separate inspection systems for each production line, Siemens uses a shared platform that can be applied consistently across factories in different regions. 

EthonAI provides the AI models and analytics framework, while Siemens provides domain knowledge and integrates its industrial software solution. This enables rapid rollout without redesigning local systems.

How it works 

From inspection to root cause insight 

The platform ingests machine data, process signals and quality measurements from existing factory data platforms. AI models analyse this information continuously to detect deviations and emerging defects. 

For visual inspection, the system can learn from a small number of reference images taken under stable conditions. This allows inspection setups to be completed in minutes rather than days. Once deployed, inspections run automatically and flag anomalies in real time. 

Beyond detection, the platform applies causal analysis to link quality issues to underlying process conditions. Engineers can move from observing a defect to understanding contributing factors without switching tools. Insights are translated into alerts and recommendations that can be acted on directly. 

How it differs from conventional approaches 

Moving beyond isolated inspections and manual analysis 

Traditional quality programmes rely on periodic audits and expert-led analysis of limited datasets. While effective in controlled settings, these methods cannot continuously interpret large-scale sensor data across multiple plants. 

The Siemens–EthonAI approach treats quality data as a continuous learning source. By analysing full data streams and applying manufacturing-specific models, the system identifies patterns that manual sampling would miss. Standardising inspection and analysis through a single platform also reduces duplication and supports consistent practices across sites.

Impact 

• More than $10 million in projected savings 
• Deployment across 20 factories, with further expansion planned 
• Inspection setup time reduced from days to minutes 
• Decrease in customer complaints due to early defect detection

Why it matters 

Scaling quality improvement across factories 

As manufacturing networks grow more complex, maintaining consistent quality requires tools that can operate at scale. The collaboration between Siemens and EthonAI shows how industrial AI can support this need by combining automated inspection with deeper process insight. 

By standardising quality analysis across factories, the platform helps organisations address inefficiencies earlier while reducing the effort required to deploy and maintain inspection systems.