CIP optimisation
Run cleaning cycles on actual need and save resources.
CIP optimisation evaluates sensor, turbidity and conductivity data to size cleaning cycles individually, cleaning only when actually needed instead of on fixed intervals.
- Cost reduction
- Higher availability
- Higher quality
water and chemical use
cleaning time
payback
asset availability
Calculated from saved water, energy and chemical costs versus fixed cleaning schedules.
- 01
Data integration
Consolidate data from ERP, MES and further sources for CIP optimisation.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the CIP optimisation model on historical data.
- 04
Pilot
Pilot CIP optimisation 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.
Energy consumption
Electricity, gas, water and steam per area.
Recipe data
Ingredients, quantities, allergens and work instructions.
Quality data
Inspection results, lab values and complaints.
Maintenance data
Faults, maintenance and spare-parts consumption.
Production manager
Uses insights directly in daily operations.
Quality management
Secures compliance with standards and regulations.
Sustainability lead
Captures and communicates sustainability metrics.
Maintenance
Plans maintenance and spare parts precisely.
Cost reduction
Higher availability
Higher quality
Better sustainability balance
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
Energy optimisation
Process data enables demand-based energy control.
Predictive maintenance
Sensor data provides wear indicators.
Predictive quality
Same data reveals quality deviations.