Operations and supply chain
Scrap, Yield & Process Optimization
Models learn from your historian, production records, and lab results which setpoints produce good units. Recommendations start in advisory mode, and camera inspection diverts defects.
The problem
Operators tune a process from experience, and the settings that worked on one shift drift on the next. Scrap is found at the end of the line or by the customer, after the material and machine time are already spent.
Who owns it
The plant or process engineering lead, with quality management. Operators and shift supervisors act on the recommendations.
What it learns from
- Process historian time series
- Production execution records
- Lab quality results
- Raw-material lots and properties
- Camera images from the line, for inspection
How it works
Predict, decide, act, measure
The same four steps in every solution, in this order, each one visible to the team that owns it.
- 01
Predicts quality from the process, and detects defects
Models learn which combinations of setpoints, materials, and conditions produce good units, and predict the quality of what is running now. Vision models inspect units on the line and flag defects.
- 02
Decides setpoints within safe bounds
Recommended setpoints stay within the operating envelope your engineers define. Advisory mode comes first, and any move to closed-loop adjustment needs evidence and an approval. Inspection thresholds balance missed defects against good units rejected.
- 03
Acts at the operator station and on the line
Recommendations appear on the operator's screen with their reasons, and defective units are diverted from the line. The solution never writes to safety systems.
- 04
Measures against comparable shifts or lines
Shifts or lines running the recommendations are compared with comparable ones that are not, on scrap, yield, defect escapes, and raw-material cost per good unit.
What it moves
The business levers
Scrap and yield
More good units from the same material and machine time.
Defect escapes
Fewer defects reaching the customer.
Material cost
Lower raw-material cost per good unit.
Process knowledge
The drivers of quality, made explicit for the engineers who own the process.
Proof
How the return is proven
The return is the change in scrap, yield, and material cost per good unit on the shifts or lines using the recommendations against comparable ones, stated against the return model agreed up front. Throughput and defect escapes are reported beside it.
What you see: the ROI readout
Every solution produces the same readout, assembled from the system's own records rather than built by hand.
Correctness
The checks the pipeline passed on your own data: leakage, point-in-time, and entity resolution.
Incrementality
The incremental effect against the holdout or equivalent design, with its confidence interval.
Return
The measured return against the investment, set beside the return model agreed before work started.
Guardrails
Complaints, cost, margin, fairness, and contact limits, each shown against its bounds.
Audit extract
Every score, action, and outcome, with the model and policy versions behind it.
Governance
What is governed
Access, approvals, and the audit trail are part of the solution, not a review step added at the end.
Identity-scoped access
Process data and recommendations are visible by plant and role through your single sign-on, with read-only access to the historian.
Approval gates
Changes to the operating envelope, and any move from advisory to closed-loop, need the process engineering lead's approval under management of change.
Audit trail
Every recommendation, whether it was applied, and the resulting quality are logged.
Safety boundary
The solution never writes to control or safety-instrumented systems, and setpoints outside the envelope are never recommended.
Why it holds up
Trained on your data, framed by the outcome
This solution trains models on your own data, checks every stage from data to decision, and is measured against the outcome it was built for. Because its predictive power is measured, it also shows the drivers behind the metric, so your team can fix causes as well as act on scores.
Why solutions hold upStart with one decision
Bring the problem and the systems your team works in. We scope the first solution with you, agree how its return will be measured, and run it in production with your people.