Calculate First Pass Yield and Rolled Throughput Yield for single or multi-step manufacturing processes. Uncover hidden factory losses and identify your weakest process steps instantly.
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FPY 97.60% with overall RTY 97.60% — process is performing well. Add more steps to calculate rolled throughput yield across your full process.
First Pass Yield (FPY) measures the percentage of units that pass through a process step correctly the first time, without requiring any rework, repair, or re-inspection. It is one of the most important metrics in lean manufacturing because it reveals the true efficiency of each process step — before rework masks the real defect rate.
Rolled Throughput Yield (RTY) extends FPY across multiple sequential process steps by multiplying the individual FPY of each step together. RTY reveals the hidden factory — the cumulative effect of small yield losses at each step that compound into a much larger overall loss. A process with five steps, each at 95% FPY, has an RTY of only 77.4%, meaning nearly a quarter of all units require rework somewhere along the line.
The key difference between FPY and traditional yield metrics is that FPY excludes reworked units. Traditional “final yield” counts reworked units as good, hiding the cost of rework. If 100 units enter a process, 10 fail inspection, 8 are reworked successfully, and 2 are scrapped, the final yield is 98% but the FPY is only 90%. The 8% rework represents hidden cost, capacity loss, and quality risk.
The hidden factory is a Lean Six Sigma concept describing the invisible rework loops, re-inspections, and workarounds that consume resources without adding value. RTY quantifies the hidden factory by showing the true probability that a unit flows through the entire process without any intervention. Companies often discover that their actual throughput yield is 20–30 percentage points lower than their final yield suggests.
FPY is used in IATF 16949, ISO 9001, and lean manufacturing environments to identify the weakest process steps, prioritize improvement efforts, and track the impact of corrective actions over time. When combined with cost data, FPY enables accurate cost-of-poor-quality (COPQ) calculations that justify improvement investments.
To improve FPY, focus on the step with the lowest individual FPY first — this is where you get the most leverage. Root cause analysis tools such as fishbone diagrams, 5-Why analysis, and designed experiments (DOE) are commonly used to identify and eliminate the sources of first-pass failures.
| FPY / RTY | Interpretation | Typical action | Status |
|---|---|---|---|
| ≥ 99% | Excellent — world-class process | Maintain and standardize | ✓ Excellent |
| ≥ 95% | Good — well-controlled process | Continue monitoring | ✓ Good |
| ≥ 85% | Marginal — rework is consuming resources | Investigate top defect types | ⚠ Marginal |
| ≥ 70% | Poor — significant hidden factory | Prioritize improvement projects | ✗ Poor |
| < 70% | Critical — process is not viable | Immediate corrective action | ✗ Critical |
The FPY calculator is just the beginning. We're building a complete Statistical Process Control platform designed for manufacturing teams who are tired of Excel.
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