Predict the next batch — not the last one.
Yield, scrap and throughput models built on your data, trained in ML Studio, deployed inside the workflow that already runs the line. No black boxes, no MLOps consultancy required.
For
Analytics / Line 3
Scenario · Yield projection · WO-2241
Scenarios
Top drivers
Most plants drown in dashboards. Few have models that move the number.
BI tells you what already happened. Predictive analytics tells you what will happen — and what to change. The gap is rarely the model. It's deploying it where the work happens.
Capabilities included.
Yield prediction, scrap analysis, throughput forecasting and machine data collection — composed and owned by your team.
Yield Prediction
Predict first-pass yield from process and material features — and pre-empt the levers that move it.
Scrap Analysis
Identify root causes of scrap. Analyze defect patterns by shift or machine to implement corrective actions.
Throughput Forecast
Forecast line and shift throughput against orders. Catch capacity gaps before planning meetings catch you.
Machine Data Collection
Connect to any PLC or sensor using vendor-independent standard protocols. Centralize data for analysis and reporting without vendor lock-in.
From data to decision.
Connect
Stream from PLCs, SCADA and ERP. RockQ joins shop-floor with planning data automatically.
Train
AutoML for tabular & time-series, or your own Python — same toolchain, same governance.
Embed
Predictions surface inside the app, MES screen or alert that already runs the line.
Improve
Outcomes feed retraining. The model learns from every run, not from a quarterly review.
Outcomes that move the P&L.
+8%
First-pass yield
Predicted drift acted on before the batch is scrap.
−30%
Scrap avoided
Operators see risk early, not after the lab result.
<6 wks
Model in production
From first dataset to live deployment, on a single platform.
Three models, in production today.
Predictive analytics is only useful when it's deployed. Here are three model archetypes our customers run on real lines — same toolchain, very different problems.
Next-batch yield
Predicts first-pass yield of the next batch from process and material features.
- Wire tension
- Operator shift
- Material lot
- Ambient temp
Predicted FPY
%, with 80% PI
Top scrap drivers
Ranks the top features pushing scrap up across the last shift, line by line.
- SPC violations
- Tool age
- Setpoint drift
- NCR history
Driver rank
+ explanation
Shift throughput
Predicts end-of-shift throughput from the first 90 minutes of run data.
- Cycle time
- Stops so far
- Mix
- Operator team
Predicted units
@ end of shift
Powered by the RockQ platform
This solution composes these platform capabilities. Each one is also available standalone.
An expert behind every solution.
Real engineers, real factory experience. Drop them a line — they'll respond, scope and propose a working architecture, not a sales deck.
Bring one number worth predicting.
Yield, scrap, throughput, OTIF — pick one. We'll build a model on your data and show the dollar impact in two weeks.