---
title: "Why reworked parts don’t count in first pass yield"
description: "Reworked parts aren’t good parts in first pass yield or OEE quality. Work FPY, final yield and scrap rate on one batch, and see why quality and scrap disagree."
category: "Operations and lean"
author: "Applications Engineering Team"
published: "2024-07-09"
updated: "2026-10-20"
source_url: "https://www.iotflows.com/blog/first-pass-yield/"
---
> Question: Should reworked parts count as good, and why does my OEE quality not match my scrap rate?

## Key takeaways

- Reworked parts are not good parts in first pass yield or in OEE quality, even when they ship.
- First pass yield, rework rate and scrap rate add up to 100% when all three divide by units started.
- OEE quality reads below 100% minus the scrap rate whenever parts are reworked, because quality also loses every reworked part.
- Rolled throughput yield multiplies the step yields, so three steps near 95% deliver about 87% of parts untouched.
- Count each part once, log every reject with a reason and a disposition, and write down every denominator.

Reworked parts should not count as good. In first pass yield and in OEE quality, a part is good only if it passed every check the first time, so a part that needed rework is a loss even after it ships. That is also why OEE quality reads lower than 100% minus your scrap rate: the scrap rate counts only the parts you threw away, and quality also loses every part that took a second pass.

This guide is for engineers who report yield and plant managers who have to explain why two reports disagree. It works one batch through every yield figure, the same week through OEE both ways, and a three-step line.

## What is first pass yield?

> **Definition.** **First pass yield (FPY)** is the share of units that pass a process step, or a whole process, the first time, with no rework, repair or retest: units good the first time divided by units started.

The IoTFlows glossary entry for [first pass yield](/docs/get-started/glossary/#fpy) applies the same idea down a line of machines, comparing each step’s count with the first step’s. Either way, the numerator should hold only parts that never left the normal path.

> **Definition.** **Final yield** is the share of units started that end up good after every repair, rework and re-inspection: units passed as good at the end, however many tries they took, divided by units started.

Three ratios describe one batch. Each has the same denominator here:

**First pass yield** = (Units good the first time) ÷ (Units started)

**Final yield** = (Units good after rework) ÷ (Units started)

**Scrap rate** = (Units scrapped) ÷ (Units started)

Write the denominator down. Some plants divide scrap by units produced, some by good units and some by units started, and each gives a different number for the same batch. This article uses units started for every ratio, so the figures add up.

## Work one batch through every yield

Take a week of one part on one machine and sort every part by what happened to it at inspection.

#### Worked example: One week of a hydraulic fitting on a CNC lathe

- 1,000 fittings machined and sent to inspection.
- 920 pass the first time.
- 80 fail. 50 are reworked at the deburr bench, re-inspected and pass. 30 are scrapped.

FPY is 920 ÷ 1,000 = 92.0%. Final yield is (920 + 50) ÷ 1,000 = 97.0%. Scrap rate is 30 ÷ 1,000 = 3.0%, and rework rate is 50 ÷ 1,000 = 5.0%.

**Result:** FPY 92.0% = 100% − 3.0% scrap − 5.0% rework

Figure: A bar of 1,000 fittings splits into 920 passed first time, 50 reworked then passed and 30 scrapped. Brackets beneath mark first pass yield at 92.0%, final yield at 97.0% and scrap rate at 3.0%.

*One batch, three yields: the 5 points between first pass yield and final yield are the rework. Sample data.*

Each figure answers a different question about the same 1,000 parts:

| Metric | Counts as a loss | Fitting batch | Answers |
|---|---|---|---|
| First pass yield | Scrap and rework | 92.0% | How well does the process run? |
| Final yield | Scrap only | 97.0% | How many parts can we ship? |
| Scrap rate | Scrap only | 3.0% | How much material is lost? |
| Rework rate | Rework only | 5.0% | How much work is done twice? |

All four divide by units started. FPY, rework rate and scrap rate add up to 100%.

The 5-point gap between FPY and final yield is the rework. Final yield never shows it, and neither does a scrap report.

## Why reworked parts don’t count as good

OEE counts good parts from the first pass. Vorne’s OEE reference defines good parts as parts that pass through the manufacturing process the first time without needing rework.[1] NIST’s guide to collecting manufacturing data lists OEE’s quality factor as the first pass yield rate.[3]

The same rule covers both quality losses among the six big losses. Process defects in stable production and reduced yield from startup to stable production each include parts that can be reworked as well as parts that are scrapped.[2]

The rule holds outside OEE too, for three reasons:

- **Rework spends capacity.** A reworked fitting used its machine cycle, then bench time, then a second inspection. For example, 50 reworks at 8 minutes each is 400 minutes, about 6.7 hours of labor that final yield records as nothing.
- **Rework hides the cause.** A step that makes 5% rework and no scrap looks clean in a scrap report, and the defect keeps coming.
- **Rework adds handling.** A repaired part is moved, measured and logged again, and each touch is a chance to damage it or mix it into the wrong lot.

Rolled throughput yield takes the same view: it screens out any unit that fails once along the way, even if it is fixed later.[4] Final yield still has a job, which the [decision table](#choose) at the end covers.

## Why OEE quality doesn’t match your scrap rate

With a 3.0% scrap rate, you might expect OEE quality of 97.0%, but on the fitting batch quality is 92.0%. The 5 points are the rework. Rework and five other causes split the two numbers:

| Cause | What happens | Fix |
|---|---|---|
| Rework | Quality loses it, scrap rate doesn’t | Report rework rate beside scrap |
| Denominator | Scrap ÷ started, ÷ made or ÷ good | Use one denominator, in writing |
| Timing | Scrap is logged at the end of the week | Log rejects when you find them |
| Scope | Downstream scrap is booked to the line | Log the step that made the defect |
| Startup rejects | Left out of the scrap report | Count them in both numbers |
| Units | Scrap in pounds, output in parts | Convert with the part weight |

The rework gap moves OEE by the same proportion as it moves quality, because OEE multiplies the three factors. The [OEE calculation guide](/blog/how-to-calculate-oee/) works each factor in full.

#### Worked example: The same week on the lathe, quality counted two ways

- Availability is 90.0% and performance is 85.0%.
- Quality from first pass yield is 92.0%. Quality from final yield is 97.0%.

OEE with first pass quality is 0.900 × 0.850 × 0.920 = 70.4%. OEE with final yield as quality is 0.900 × 0.850 × 0.970 = 74.2%.

**Result:** Counting rework as good adds 3.8 points of OEE

Figure: Two OEE bars for the same week on the lathe. With quality from first pass yield at 92.0%, OEE is 70.4%. With quality from final yield at 97.0%, OEE is 74.2%, and the extra 3.8 points are highlighted as rework counted as good.

*Same week, same parts: counting reworked parts as good lifts OEE by 3.8 points. Sample data.*

Quality in the IoTFlows glossary is the [share of parts made that were good](/docs/get-started/glossary/#quality), and a quality figure means the same thing across machines only when every machine uses first-pass good parts.

> **Count a reworked part only once**
>
> When a reworked part runs through the machine again, the machine counts a second cycle but no new part. Keep the total count at one per part, on its first pass, and log the rework cycle on its own. Otherwise the total grows and quality reads as if the batch were bigger than it was.

## Yield across a line of steps

When a part passes through several steps, each step has its own FPY, and the line’s first pass yield is their product. *Rolled throughput yield* (RTY) is the probability that a unit makes it through the entire process without a single defect.[4]

**Rolled throughput yield** = FPY step 1 × FPY step 2 × FPY step 3

#### Worked example: 5,000 fittings through three steps

- OP10, turn: 5,000 start and 4,800 pass the first time, so FPY is 96.0%.
- OP20, mill: 4,800 start and 4,560 pass, so FPY is 95.0%.
- OP30, inspect: 4,560 start and 4,332 pass, so FPY is 95.0%.

The average step FPY is 95.3%. RTY is 0.960 × 0.950 × 0.950 = 86.6%, and 4,332 ÷ 5,000 gives the same 86.6%.

**Result:** RTY 86.6%: 668 of 5,000 fittings left the normal path at least once

Figure: Three steps in a row: OP10 turn at 96.0% first pass yield, OP20 mill at 95.0% and OP30 inspect at 95.0%. The average step yield is 95.3%, but rolled throughput yield is 86.6%, with 4,332 of 5,000 parts passing every step the first time.

*Every step looks close to 95%, and the line delivers 86.6% of its parts untouched. Sample data.*

The loss compounds quickly. iSixSigma’s example of four steps, each with a 15% first-pass failure rate, gives an RTY of 52.2%: about half the units reach the end without a defect.[4] Start with the one or two steps with the lowest FPY, where iSixSigma places the largest opportunity to raise RTY.[4]

[A counter after every step](/blog/production-line-monitoring-without-plcs/#every-step) measures this directly, with no multiplication: the count after the last step divided by the count entering the first. That works when rejected parts leave the line at the step that caught them, as on a bottling line where each machine ejects its own rejects.

See [how to monitor a packaging line](/blog/how-to-monitor-a-packaging-line/) for where to put the counters. Return reworked parts after the last counter, or log them apart, so a repaired part never reads as a first pass.

## Log scrap and rework so the numbers agree

Most yield disputes are logging problems, not math problems. These five habits make FPY, final yield, scrap rate and OEE quality reconcile:

1. **Count every part once, at the machine.** Take the total count from the machine’s own cycle, not from a tally sheet. Check a new count against a hand count of 10 parts, and if it reads double or half, look for a parts-per-cycle setting before blaming the counter. See [Troubleshoot inaccurate part counts](/docs/production/troubleshoot-part-counts/).
2. **Log every reject when you find it.** Record the reject against the job and the step that made the defect, with a reason, during the shift. A reason list works best short, with one cause per code, the same rules that make [downtime reason codes](/blog/downtime-reason-codes/) useful.
3. **Record the disposition.** Mark each reject as scrap or rework. Without the disposition, you can calculate scrap rate or FPY, but not both.
4. **Keep rework cycles out of the total.** Log a rework pass as rework, not as a new part, so the total count stays one per part.
5. **Report the yields side by side.** Show FPY, final yield and scrap rate in one table, each with its denominator. A reader who sees all three never has to ask why they differ.

## Measure yield at every step

IoTFlows counts every part at every step of a line, with a BeamTracker on each step and no PLC connection. Grouped into a cascade, each step after Step 1 reports FPY as its count over Step 1’s count, with the parts lost and the rate difference beneath it, live during the shift. See [FPY across a cascade](/docs/monitoring/asset-groups/#fpy).

Quality reads as good parts over everything counted, from the [scrap operators log](/docs/production/log-scrap/) with a reason against the job. Altium Packaging reads first pass yield, bottles lost and rate difference on every step of its ten bottling lines this way. See [production monitoring](/solutions/production-monitoring/).

> Now we can talk through issues, help our operators and adjust operations in real time.
>
> Steve Petti, Plant Manager, Altium Packaging ([story](https://www.iotflows.com/customers/altium-packaging/))

## Which yield to report

Each yield answers one question. Pick the one that matches the decision in front of you:

| Yield | Use it for | Don’t use it for |
|---|---|---|
| First pass yield | Improving a step; OEE quality | Planning material |
| Final yield | Material and shipment planning | OEE or process improvement |
| Scrap rate | Costing lost material | Judging a step that reworks |
| Rolled throughput yield | Comparing whole lines | Finding the failing step alone |

Default to FPY in any report about how the process runs, and put final yield beside it when the same report plans shipments. Use RTY when a part passes through two or more steps. You don’t need RTY for a single-machine cell: there it equals FPY.

When your OEE quality and your scrap rate disagree, check the gap against your rework rate first. If rework explains it, both numbers are right, and the report needs a third row.

## Questions

### Is first pass yield the same as OEE quality?

Close, with a difference in scope. OEE quality is the good count over the total count for one machine or line, where good means right the first time, so it is first pass yield measured at that machine. A plant-level first pass yield can also include steps OEE never sees, such as final inspection or a customer test. With the same counts and the same rule for good, the two match.

### Do startup rejects count against first pass yield?

Yes. Parts made between a startup or changeover and stable production are a quality loss in OEE, whether you scrap them or rework them. Leaving them out of the count raises quality and hides what each changeover costs. Log them under their own reason so you can see them apart from the rejects made in stable running.

### Should a part that fails a test and passes a retest count as good?

Not for first pass yield. A retest pass with no repair still means the first check failed. Count it as a first-pass failure and log retest passes under their own reason. A high retest pass rate often points at the gauge or the test method rather than the process, which is worth knowing before anyone adjusts the machine.

### What is a good first pass yield?

No single benchmark is worth chasing, because first pass yield depends on how strict each inspection is and how many steps it covers. Compare each step with its own history and with what your customer requires, and watch the trend. A step whose first pass yield falls week over week needs attention, whatever the absolute number.

## Sources

1. [OEE Calculation: Definitions, Formulas, and Examples](https://www.oee.com/calculating-oee/), Vorne Industries (OEE.com), accessed Oct 16, 2026
2. [Six Big Losses in Manufacturing](https://www.oee.com/oee-six-big-losses/), Vorne Industries (OEE.com), accessed Oct 16, 2026
3. [NIST AMS 300-11: Recommendations for Collecting, Curating, and Re-Using Manufacturing Data (Helu and Hedberg, 2020)](https://nvlpubs.nist.gov/nistpubs/ams/NIST.AMS.300-11.pdf), National Institute of Standards and Technology, accessed Oct 16, 2026
4. [Rolled Throughput Yield (RTY): Make Sure Your Production Is Making the Grade](https://www.isixsigma.com/dictionary/rolled-throughput-yield-rty/), iSixSigma, accessed Oct 16, 2026