Prepare line signals
Pin process parameters, material lots and measured yield or quality per batch or shift.
Industry blueprint
Industrial operations
Process and yield optimisation from plant signals. The model recommends; engineers decide; nothing controls equipment.
Yield and quality vary with hundreds of process parameters across shifts and lines. A model that learns which settings produced good output can recommend setpoints, but it must be retrained as materials and equipment change and must never act on its own. This design keeps the recommendation behind an engineer's approval.
Illustrative pipeline
Train a candidate
Run the partner's model predicting yield or quality from process settings.
Compare on held-out shifts
Score the candidate against the current model on the same held-out production periods.
Engineer approves
Recommendations reach operators only after a process engineer reviews them. No autonomous control.
What the record keeps
The signal windows, material lots, the candidate, held-out comparison, the recommendations, and the approver.
Partner brings
- Process historian and MES access
- Material and quality data
- Yield or quality model
Cairn brings
- Pinned signals and quality checks
- Held-out comparison per line
- Bounded retrain loop with approval
- A record of every recommendation
Business value to test
Yield, first-pass quality and scrap per line.
Keep the decision explicit
A recommendation is a proposal to an engineer, recorded with the model version that produced it.
Close the feedback loop
Each shift's measured outcome becomes training data for the next candidate.
Reference design, not a shipped domain pack. Setpoint changes stay in the plant's control system and under the engineer's authority.
See how failure can trigger a reviewed improvement cycle →Find the settings that make good product.
Talk about your process data, quality measurements and who owns setpoints.