• Holistic root cause analysis: Analyze process and design data together – across the entire production flow.

• Fully automated quality monitoring: Continuous root cause detection at component level.

• Early detection of critical patterns: Uncover hidden dependencies, e.g., between soldering parameters, material batches, and AOI results.

• Significantly reduce defect rates and scrap: Improve first-pass yield, reduce rework, and lower production costs.

The Solution: Causal Intelligence for Complex Manufacturing

Our patented ObjectAnalytics database integrates design and machine data, test results (e.g., ICT, AOI, SPI), material records, and environmental variables into a complete, object-centric view of production. It enables real-time analysis of relationships between design, process steps, components, soldering processes, and failure patterns.

The CausalDiscoverer identifies true root causes behind issues such as solder bridges, cold joints, or intermittent testing failures – even in highly complex multilayer PCB production.

The Causal DiscoveryBot continuously monitors your production data. As soon as new causal factors emerge – like a specific material lot linked to rising defect rates – it instantly notifies the right teams via email or messaging tools.

This allows for immediate, targeted interventions – before problems escalate into widespread production losses.

Why Xplain Data stands out in Electronics Manufacturing Optimization:

• Scalable for complex lines: Thousands of variables, fast takt times, multi-step processes – we deliver real-time insight at industrial scale.

• Object-centric data structure: Trace each defect back to the exact process step or condition that caused it.

• Causal models, not just statistics: Move beyond misleading correlations to uncover true cause-and-effect relationships.

• Process change simulations: Run “what-if” analyses to test how changes e.g. to oven profiles or placement speeds would affect outcomes.

• Open integration: Connect easily with your existing tech stack – via Python, WebAPI, or directly into MES/SPC systems.