Manufacturing
Quality and yield forecasting
Predict yield, scrap, rework, and defect pressure from process conditions, materials, equipment, and supplier variation.
Sound familiar?
- Yield loss found at final inspection, hours of production too late
- Material-lot problems traced only after the scrap bin fills
- Quality costs explained at month end instead of prevented
First-pass yield
first-pass yield · Live forecast
Driver
Process parameters
Driver
Material lot
Driver
Equipment state
Forecast horizon
Hours–8 weeks
Refresh cadence
Hourly or daily
Built for
Quality engineering · Process engineering
What you can predict
One forecast can answer several operational questions.
Connect inspection results with process parameters, material lots, equipment state, operators, environmental conditions, and product mix to anticipate quality losses.
First-pass yield
Defect and scrap volume
Rework demand
Quality cost
Questions teams need answered
- Which line is likely to lose yield?
- What process change explains defect growth?
- Which material lots create risk?
- How much good output will remain after quality loss?
Data that can improve the forecast
Start with the history you already have. Add internal or external drivers only when backtesting shows that they improve the forecast on held-out periods.
What-if planning
Test a change before committing to it.
Compare a proposed change with the current baseline. See the expected direction, timing, range, and the assumptions behind the result.
Use a different material lot
Compare yield and defect outcomes
Tighten a process setting
Estimate quality gain and throughput impact
First-pass yield
first-pass yield · Scenario comparison
What if
Use a different material lot?
Driver
Process parameters
Driver
Material lot
Driver
Equipment state
From forecast to action
Keep the people making the decision in the loop.
01 · MONITOR
Forecast continuously
Refresh first-pass yield on a hourly or daily cadence as new data arrives.
02 · NOTIFY
Alert on meaningful changes
- Yield is forecast below specification
- Defect volume exceeds inspection capacity
03 · DECIDE
Put the result to work
From the handbook
How the forecast is actually built.
Guide
What is demand forecasting?
Demand forecasting estimates how much of a product or service people will want in a coming period, so you can buy, staff, and price before the rush — not after it has already walked in.
Guide
Time series forecasting, explained
Time series forecasting predicts the next values of something that was recorded in order — sales by day, load by hour, tickets by week — by learning from its own past, and from anything that regularly moves with it.
Guide
WAPE, explained
WAPE (weighted absolute percentage error) is total absolute error divided by total actuals. It behaves when some periods are zero, and it does not let tiny SKUs dominate the score the way MAPE does.
Build a quality and yield forecast with your data.
Start with sample data, connect your own history, or talk with us about your target, horizon, and production requirements.