First Pass Yield: FPY Formula, Benchmarks & OEE Link

What is first pass yield?

First pass yield (FPY) — also called first-time-right rate or throughput yield — is the percentage of units that complete a process step without any defect, rework or scrap. It answers a single, brutal question: "Of everything we started, how much came out right the first time?" An FPY of 95 % sounds excellent — until you multiply it across 8 process steps and discover that your rolled throughput yield is only 66 %. FPY is the quality metric that reveals hidden rework loops the scrap report never shows.

How is FPY calculated?

FPY = Good Units (no rework, no scrap) / Total Units Entering the Process

Worked example — stamping press + deburring + inspection:

• Units entering press: 1,000
• Units that required rework (burr removal beyond spec): 60
• Units scrapped: 15
• Units passed right first time: 1,000 − 60 − 15 = 925

FPY = 925 / 1,000 = 92.5 %

Critical distinction: those 60 reworked units eventually shipped as good parts. Traditional yield (final yield) would report 985 / 1,000 = 98.5 %. The plant looks great. But FPY shows the truth: 7.5 % of production consumed extra labour, extra cycle time and extra machine capacity. That hidden cost is invisible in the scrap report — and it is the reason FPY exists.

What is rolled throughput yield (RTY) and why does it matter more than single-step FPY?

Most manufacturing processes have multiple steps. RTY multiplies the FPY of each step to show the probability that a unit passes the entire process without any rework at any point.

RTY = FPY₁ × FPY₂ × FPY₃ × … × FPYₙ

Every step looks individually acceptable (93–98 %). But only 75.8 % of all units pass all six steps right the first time. Nearly one in four units touches a rework loop somewhere. That is the hidden factory — the capacity consumed by doing things twice that should have been done right once. RTY makes it visible. Individual step FPY does not.

How does FPY relate to OEE?

FPY feeds directly into the Quality factor of OEE. OEE Quality is calculated as good parts / total parts. Parts that are reworked and eventually pass count as "good" in the final yield — but they consumed extra cycle time (lowering Performance) and extra machine availability (lowering Availability if a machine was tied up for rework). FPY exposes what the OEE Quality number alone hides.

In Christian Fieg's book OEE: Eine Zahl, viele Lügen, this is one of the central arguments: an OEE Quality of 99 % can coexist with an FPY of 85 % — if 14 % of units were reworked and eventually passed. The OEE number looks great; the process is burning capacity. FPY tells the truth.

What FPY benchmarks should a manufacturer target?

The goal is not a universal target number — it is directional improvement. If your stamping FPY is 91 % today, the next milestone is 94 %. Neoperl achieved a 15 % scrap reduction through PLC alarm correlation combined with defect classification — which translates directly into FPY improvement, because fewer defects mean fewer rework loops.

How does an MES automate FPY measurement?

Manual FPY tracking means: operators tally defects and rework on paper, someone enters the numbers into Excel at the end of the shift, and the quality manager sees results 24–48 hours later. By then, the root cause is cold. An MES captures FPY in real time:

• Automatic count capture: Good parts, scrap parts and rework events are logged directly from the PLC — no manual tally, no end-of-shift data entry, no transcription errors.
• Defect classification at the source: When a part is rejected, the operator (or the machine itself via alarm codes) classifies the defect type. The MES builds the Pareto automatically: which defect types drive the most FPY loss?
• Step-by-step FPY → automatic RTY: The MES tracks FPY at each process step and calculates RTY across the entire line. The hidden factory becomes visible on a single dashboard.
• Correlation with process parameters: FPY drops from 96 % to 89 % on Thursday night shift? The MES correlates: which material lot was running, which process parameters drifted, which alarms fired. At Neoperl, exactly this correlation — PLC alarms mapped to quality defects — identified 4 alarm codes that accounted for 80 % of all losses.
• Real-time alerts: When FPY falls below a defined threshold (e.g., < 90 % rolling last 100 parts), the system notifies the shift leader immediately — not at the morning meeting tomorrow.

The SYMESTIC production metrics module captures good/reject counts per machine per order automatically. Combined with the process data module, it provides the defect-to-cause correlation that turns FPY from a lagging KPI into an actionable signal.

FAQ

What is the difference between FPY and final yield?
Final yield counts everything that eventually ships as good — including units that were reworked. FPY counts only units that passed right the first time. Final yield flatters the process; FPY tells the truth. A process with 98 % final yield and 85 % FPY has a 13-percentage-point rework problem that is consuming capacity, labour and material without appearing in the scrap report.

How does FPY connect to DPMO and sigma level?
FPY is a unit-level metric (did the whole unit pass?). DPMO is an opportunity-level metric (how many defect opportunities per million?). A unit can fail FPY due to a single defect out of 20 possible defect opportunities. DPMO tells you which specific opportunity is failing most often; FPY tells you the overall process result. Use FPY for line-level performance tracking. Use DPMO for DMAIC root-cause analysis when you need to drill into which defect types to attack first.

Should reworked units be counted as FPY failures even if they pass final inspection?
Yes — always. That is the entire point of FPY. A reworked unit consumed extra resources. If you count it as a pass, you are measuring final yield, not FPY, and the hidden factory stays hidden. The discipline of counting rework as an FPY failure is uncomfortable — it makes the numbers look worse. But it is the only way to see the true cost of quality and drive real improvement.

Related: OEE Explained · DPMO · Six Sigma · DMAIC · Control Limits · MES: Definition & Functions · SYMESTIC Production Metrics

About the author

Christian Fieg

Head of Sales at SYMESTIC. Six Sigma Black Belt. Author of OEE: Eine Zahl, viele Lügen. Previously Johnson Controls (900+ machines, global MES rollout), Visteon, iTAC, Dürr. · LinkedIn