Quality control loops
Detect quality deviations early and correct them automatically.
Quality control loops monitor process and lab values in real time, automatically adjusting plant parameters or triggering alerts when deviations occur.
- Higher quality
- Cost reduction
- Faster processes
scrap
rework
payback
early detection
Calculated from reduced scrap, lower inspection effort and fewer complaints.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Quality control loops.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Quality control loops model on historical data.
- 04
Pilot
Pilot Quality control loops 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.
Lab data
Analyses, measured values and test protocols.
MES data
Production orders, feedback and machine data.
Recipe data
Ingredients, quantities, allergens and work instructions.
Quality data
Inspection results, lab values and complaints.
Historian data
Time series from the process control level.
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
Easier compliance
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.
Digital twin
Models can be reused in simulations.