Most manufacturing AI conversations start with visual inspection — and stop there. While computer vision for defect detection is valuable, it represents less than 20% of the quality management opportunity. The real value is in connecting quality data from the shop floor to enterprise planning systems, enabling predictive quality management that prevents defects rather than detecting them.
From Detection to Prevention
DGT's approach to manufacturing quality goes beyond inspection:
- Process parameter optimization: ML models that identify the process conditions that lead to defects before they occur
- Supplier quality prediction: AI that scores incoming material quality based on supplier history, lot data, and environmental conditions
- Root cause acceleration: NLP models that analyze quality records, maintenance logs, and operator notes to identify root causes 5x faster than manual investigation
- Enterprise feedback loops: Integration with SAP QM and S/4HANA to close the loop between quality data and production planning
The Data Foundation Requirement
Predictive quality management requires clean, connected data from MES, SCADA, ERP, and quality management systems. Most manufacturers have this data — but it lives in silos. DGT's DataBridge accelerator connects these systems and creates the unified data layer that AI models need to deliver enterprise-scale quality improvements.