Process Parameter Optimization
Find the best machine settings using data.
Process parameter optimization uses historical production data to find optimal settings for throughput, quality and energy use. Instead of relying on individual experience, it produces data-driven recommendations, reducing variance and increasing yield.
- Higher and more stable yield
- Less dependency on individual expert knowledge
- Reduced energy and material consumption
yield
process variance
payback period
energy use per unit
Calculated from current yield variance per line and the potential of optimized parameter sets.
- 01
Define target metrics
Define optimization targets together with process engineers.
- 02
Data preparation
Combine parameter and outcome data from MES and historian.
- 03
Optimization model
Model the relationship between parameters and target metrics.
- 04
Scenario simulation
Test parameter sets virtually before going live.
- 05
Operational rollout
Gradually validate recommendations together with operators.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
MES process parameters
Actual settings per order and batch.
Historian time series
Continuous process data over time.
Quality results
Target metrics to evaluate parameter sets.
Energy metering data
Consumption per process step.
Material data
Batch-specific properties as influencing factors.
Operator inputs
Manual corrections as an additional signal.
Process engineers
Gets solid optimization proposals instead of gut feeling.
Production management
Sees measurable gains in yield and efficiency.
Energy management
Benefits from reduced specific energy consumption.
Shift operators
Gets clear, actionable guidance in the control room.
Higher and more stable yield
Less dependency on individual expert knowledge
Reduced energy and material consumption
Faster onboarding of new operators
Objective basis for process decisions
With a solid data foundation this use case gets faster, cheaper and far more stable.
Unified data foundation across MES, historian and energy systems
Fast simulation of new parameter sets without production risk
Reusable optimization logic for new lines
Automated recommendations delivered directly to the control room
Predictive quality
Optimized parameters directly reduce scrap rates.
Energy optimization
The same models also surface energy-saving potential.
Digital twin
Optimized parameters can be further tested in the digital twin.