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No Rework or Retests: Measure First Pass Yield on the Shop Floor

October 12, 2026
No Rework or Retests: Measure First Pass Yield on the Shop Floor

First pass yield (FPY) is the percentage of units that complete a process step correctly the first time, with no rework, retest, or scrap, and it is one of the clearest signals of hidden rework cost and lost capacity on a production floor. Both ASQ and ISO treat it as a foundational manufacturing KPI, and we see it as the starting point for any serious yield improvement effort.

TL;DR:

  • For a shift starting 500 parts, 460 first pass successes produce 92% FPY; 30 reworked parts and 10 scrapped parts still count as failures.
  • Track FPY by station to locate problems; use RTY, the product of step yields, to assess a unit’s chance of passing the line without rework.
  • Set targets only after baseline measurement, capability assessment, and risk review; action limits should reflect process complexity, customer specifications, and rework costs.
  • Keep the process boundary, time window, and part family fixed, and count every retest or rework as a failure; reason codes enable root cause analysis.
  • A NIST case cut processing time by more than two thirds without capital spending, using process mapping, root cause analysis, simpler documents, and visual controls.
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Table of Contents

1. What is first pass yield?

First pass yield measures how many units pass through a defined process step, meeting every requirement, without needing rework, a retest, a diverted repair path, or outright scrap. The ASQ quality glossary defines FPY as units passing the first time without rework divided by units entering the process, expressed as a percentage. That definition sounds simple, but the measurement boundary determines whether the number means anything.

Before collecting data, a quality engineer has to decide exactly where the process starts and ends, and what counts as a failure. The following events typically count against FPY, even when the unit eventually ships:

  • A unit sent back for rework, however minor.
  • A unit that fails initial inspection and is retested after adjustment.
  • A unit diverted to an offline repair station.
  • A unit scrapped outright.

Skip this step and two shifts measuring the same line will report different numbers for the same quality performance.

FPY, first time yield (FTY), and rolled throughput yield (RTY) often get used interchangeably, which causes real confusion on the shop floor. FPY and FTY are generally the same calculation applied at a single process step: the share of units that pass that step clean. RTY is different. It is the product of every step's FPY across a multi-step process, so it represents the probability that a unit moves through the entire line defect-free without any rework at any point, as ISSSP explains.

That distinction matters because rework hides inside individual steps. Use these metrics with a clear purpose in mind:

  1. Track FPY at each station to catch operational problems close to where they happen.
  2. Track RTY across the full routing when you need an honest end-to-end defect-free probability.
  3. Report RTY to customers or leadership when overall process health, not station-level detail, is the question.

3. How to calculate FPY: formula and worked examples

The formula is straightforward: FPY equals the number of units passing a step the first time with no rework, divided by the number of units entering that step, multiplied by 100, per the ASQ definition.

Say a machining cell starts 500 parts in a shift. Of those, 460 pass inspection on the first attempt, 30 need rework before they pass, and 10 are scrapped. Only the 460 count in the numerator. FPY equals 460 divided by 500, times 100, which is 92%. The 30 reworked parts and 10 scrapped parts all count as first-pass failures, even though 30 of them eventually ship.

Proportional breakdown of first-pass results for 500 parts

Multi-step processes reveal more. Picture a three-step assembly line where each step has its own FPY:

The hidden rework and scrap burden is what a single-step view never shows.

4. Interpreting FPY: how to set targets and what "good" means

There is no universal FPY target that applies across every product, process, or industry, and chasing one is a common mistake. ISO KPI guidance requires that any manufacturing KPI, FPY included, carry a defined formula, consistent units, a stated time window, and action limits set by the organization itself, not borrowed from an industry average.

What should actually shape your target:

  • Process complexity and the number of operations the unit passes through before final acceptance.
  • Risk category: a flight-critical aerospace component carries different stakes than a bracket.
  • Customer specification tightness relative to your demonstrated process capability.
  • The real cost of rework versus scrap for that specific part family.

A defensible approach runs baseline measurement first, then a capability assessment against your tolerances, then a risk review tied to customer specs, and finally staged improvement targets rather than one aggressive jump. Our guide to manufacturing quality KPIs walks through how to document these limits so they hold up in an audit or a management review.

5. Measuring FPY reliably: data collection rules and common pitfalls

Consistent FPY tracking depends on a few disciplined rules applied the same way every shift. The measurement boundary, the time window, and the part family grouping all need to stay fixed once you start comparing results over time.

  1. Lock the measurement boundary first: decide exactly which operations fall inside the step you're tracking and document it.
  2. Keep the time window stable, whether that's per shift, per batch, or per day, so trend lines mean something.
  3. Separate part families and routings rather than blending them into one average that hides problem areas.
  4. Never let a passed retest quietly become a first-pass success; the original failure still counts.

The most common pitfall is changing the denominator mid-stream, adding scrapped units back in or dropping reworked units out once the pressure to show improvement sets in. A second common mistake is mixing dissimilar part families into one FPY number, which flattens out the signal from the parts actually causing trouble.

Shop-floor capture methods matter as much as the rules. Operator tablets with barcode or serial scanning cut transcription errors, automated CMM import eliminates manual re-entry of dimensional results, and tagging each rework event with a defect reason code turns raw FPY numbers into a stratified Pareto you can actually act on.

Pro Tip: Tag every rework and scrap event with a reason code at the moment it happens, not at end of shift. Retroactive tagging almost always loses the detail that root-cause analysis needs.

6. How to improve first pass yield: prioritized tactics

Start with visibility before spending on anything else. A value-stream map paired with a stratified defect Pareto, broken out by machine, shift, and defect mode, usually points straight at where most of the loss is concentrated, and it costs nothing but time.

Once you can see where failures cluster, move into root cause:

  • Apply 5 Whys and a fishbone diagram to the top Pareto category, not the whole defect list at once.
  • Tie each recurring failure back to a specific measured characteristic rather than a general complaint.
  • Use statistical process control to catch drift inside the process, before parts fail inspection.
  • Build mistake-proofing and standardized work instructions around the failure modes you've confirmed.
  • Tighten incoming inspection and supplier quality checks, since poor incoming material quietly drags FPY down regardless of how well your own process runs.

Run improvements as small-cycle PDCA experiments: change one variable, measure FPY over a stable window, and confirm before scaling the change. Watch RTY alongside FPY whenever steps interact closely, since fixing one station can shift the bottleneck to the next.

Pro Tip: Fix the highest-volume defect category first, even if it isn't the most dramatic one. Volume moves the Pareto faster than severity does.

7. Evidence and case study: what real FPY improvements look like

A NIST Manufacturing Extension Partnership success story documents a manufacturer that significantly improved FPY, dramatically cut lead time, and reduced processing time by more than two-thirds, all through value-stream mapping, root-cause work, simplified documentation, and visual management, not capital investment.

That case reinforces a pattern we see across the industry: the biggest early wins come from removing friction in the process itself.

  • Map the flow before changing anything, so the real bottleneck is visible.
  • Simplify documentation and handoffs that slow operators down without adding quality.
  • Add visual management so deviations get caught at the station, not three steps later.

A NIST report on quality information frameworks adds a design-side piece to this picture, describing how digital thread practices trace manufacturing failures back to design tolerances rather than treating every failure as a shop-floor problem alone.

8. Where FPY belongs in your 2026 improvement portfolio

Treat FPY as an early-warning KPI, not a scorecard number. Its real value is in feeding design and tolerance conversations: when a characteristic keeps failing first pass, that's a signal for closed-loop tolerance engineering, not just tighter inspection. Favor small, fast experiments over sweeping changes, and keep the data fresh enough to trust.

— Michael Chen

9. How a quality inspection platform supports FPY measurement

Measuring FPY consistently across shifts, part families, and inspection stations is exactly the kind of structured data problem we built QA-Report to handle. A measurement wizard can link ballooned drawing dimensions directly to measured results, auto-flag anything out of tolerance, and generate statistical summaries that support an FPY calculation instead of relying on a spreadsheet someone forgets to update.

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A few capabilities that apply directly here:

  • Automatic drawing ballooning and a built-in 3D CAD viewer remove hours of manual setup before inspection even starts.
  • CMM data import pulls measured results straight into the report, cutting the transcription errors that quietly distort FPY numbers.
  • FAI and GD&T reporting generate audit-ready documentation that satisfies ISO 9001, AS9100, and PPAP requirements.
  • An integrated MES layer tracks batches, operations, and rejection and recovery data, so first-pass failures get tagged with a reason code at the point of occurrence.

Try the free drawing ballooning tool to see the measurement workflow firsthand, or check our pricing plans, which range from a Free tier up through PRO at $149.99 per month, with On-Premise and Enterprise Solutions available for teams that need custom deployment.

FAQ

What is a good First Pass Yield?

There is no single universal target, since ISO KPI guidance requires action limits to reflect your own process capability, risk level, and customer specifications. A high-risk aerospace characteristic and a low-risk bracket dimension will reasonably carry different FPY targets even on the same shop floor.

What is the meaning of First Pass Yield?

First pass yield means the percentage of units that complete a process step correctly on the first attempt, with no rework, retest, or scrap, as defined by ASQ. It is a step-level measure of how much hidden rework cost and lost capacity a process is absorbing.

What is the difference between First Pass Yield and first time right?

First pass yield and first time right describe the same underlying idea: a unit passing a step without any rework, retest, or correction on the first attempt. Teams sometimes use "first time right" informally on the floor while FPY and FTY remain the standard terms in ASQ's glossary and in formal reporting.

How to improve First Pass Yield?

Start with visibility through value-stream mapping and a stratified defect Pareto, then apply root-cause tools like 5 Whys and fishbone analysis to the highest-volume failure category. The NIST MEP case shows that process friction removal, simplified documentation, and visual management often deliver the largest early gains before any capital investment.

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