Predictive quality
Spot scrap before it happens.
Predictive quality links process parameters to quality outcomes to catch deviations early, so settings can be corrected before defective parts are produced.
- Higher quality
- Cost reduction
- Faster processes
scrap rate
rework effort
payback
early detection
Calculated from reduced scrap, lower rework effort and fewer complaints.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Predictive quality.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Predictive quality model on historical data.
- 04
Pilot
Pilot Predictive quality in one area and collect feedback.
- 05
Rollout
Scale the solution and integrate it into operational processes.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Sensor data
Real-time IoT and process sensors.
MES data
Production orders, feedback and machine data.
Quality data
Inspection results, lab values and complaints.
Product data
Bills of materials, variants and life cycles.
Machine data
Condition, parameters and production counters.
ERP data
Master and transaction data from ERP.
Quality management
Secures compliance with standards and regulations.
Production manager
Uses insights directly in daily operations.
IT
Builds on a scalable and secure data infrastructure.
Controlling
Quantifies effects and supports budgeting.
Higher quality
Cost reduction
Faster processes
Higher customer satisfaction
Higher efficiency
With a solid data foundation this use case gets faster, cheaper and far more stable.
Central data platform
Real-time data integration
Scalable analytics pipelines
Reusable data products
Predictive quality
Same data reveals quality deviations.
OEE monitoring
Availability data complements process metrics.
Predictive maintenance
Sensor data provides wear indicators.