---
title: "What is a good OEE score?"
description: "85% OEE is a TPM target, not a measured average. See where it comes from, what published data shows plants score, and how to set a goal from your own baseline."
category: "OEE and metrics"
author: "Data Analytics Team"
published: "2026-10-05"
updated: "2026-11-02"
source_url: "https://www.iotflows.com/blog/good-oee-score/"
---
> Question: Is 85% OEE a measured benchmark, and what should a job shop or a high-volume line expect?

## Key takeaways

- 85% OEE is the product of Nakajima’s TPM minimums: 90% availability, 95% performance and 99.9% quality.
- Published OEE measurements sit lower, from 20–30% on one packaging line to about 76% across 15 presses.
- Job shops lose availability to frequent setups, so their OEE ceiling sits below a high-volume line’s.
- Set each machine’s first OEE goal 5 to 10 points above its own baseline, and pick the end from its trend.
- Chase the factor that loses the most minutes on each machine, starting with the machine that limits output.

85% OEE (overall equipment effectiveness) is a target, not a measured benchmark. It is the product of the minimums Seiichi Nakajima set in his TPM work: 90% availability, 95% performance and 99.9% quality. Published measurements sit lower, from 20–30% on one packaging line to about 76% across 15 presses. Set each machine’s goal from its own baseline and trend instead.

This post traces the number to its source, lists the public measurements, and shows why a job shop and a high-volume line start from different places. For the definitions, start with [what OEE measures](/blog/what-is-oee/).

## Where does the 85% figure come from?

The 85% comes from total productive maintenance (TPM), the program Seiichi Nakajima led at the Japan Institute of Plant Maintenance from the early 1970s.[2] In his 1984 book *Introduction to TPM*, he gave four world-class numbers: OEE above 85%, availability above 90%, performance above 95% and quality above 99.9%.[1]

> **Definition.** **World-class OEE** is the 85% OEE figure from Seiichi Nakajima’s total productive maintenance work, the product of minimum targets of 90% availability, 95% performance and 99.9% quality that he set for plants to strive for.

The [glossary entry for OEE](/docs/get-started/glossary/#oee) defines the three factors the way IoTFlows measures them.

**World-class OEE** = 90% availability × 95% performance × 99.9% quality = 85.4%

Check it the other way round: 95% × 99.9% is 94.9%, and 90% of 94.9% is 85.4%. Many pages use 99% quality instead, which gives 0.90 × 0.95 × 0.99 = 84.6%. Both round to the 85% people quote.

The factor targets aren’t equal, and that matters. Three factors at 90% each give only 72.9%, so 85% needs near-perfect quality and close-to-ideal speed on top of few stops.

Nakajima set these numbers from practical experience as minimums for plants to strive for. He also noted that every plant winning Japan’s Distinguished Plant Prize for TPM scored above 85%.[2] The figure describes award-winning TPM plants in Japan in the 1970s, in discrete manufacturing rather than process industries.[2] It was never an average across plants.

## What do plants actually score?

Public measurements are few, and each covers a small number of machines under its own definitions. These four name their setting and their source.

| Measurement | Setting | OEE |
|---|---|---|
| Ljungberg, 1998 | About 20 cases | About 55% |
| Raju et al., 2022 | 15 presses in a tool and die shop | 76.0% average |
| NIST MEP, 2022 | One CNC lathe at an aerospace plant | 39%, then 45% |
| NIST MEP, 2022 | One snack packaging line | 20–30%, then above 50% |

Each row is one study or one project, not a sample of an industry. The NIST rows give OEE before and after an improvement project.

Ljungberg found OEE of only around 55% across about 20 cases.[3] The 15 presses in Raju’s tool and die shop averaged 75.99%, with availability at 87.02%, performance at 87.34% and quality at 99.98%.[1] Quality beat Nakajima’s target there. Stops and speed losses kept OEE 9 points short.

The two NIST MEP projects start lower. Leggett & Platt Aerospace lifted OEE on its most problematic CNC lathe from 39% to 45% with a TPM program.[4] Pladis Global’s snack packaging line in Horseheads, New York, started at 20–30% and passed 50%, against a goal of more than 60%.[5]

Figure: Four published OEE measurements on a 0 to 100% scale against a dashed 85% target: a packaging line at 20–30% rising above 50%, a CNC lathe from 39% to 45%, about 55% across 20 cases, and 76% across 15 presses.

*Every published measurement here sits below the 85% target, including the best of them, 15 presses at 76%.*

And the 60% average that many pages repeat? None of them cites a survey, and the closest published measurement is the 55% across about 20 cases above.[3] Quote 60% as a rule of thumb, if at all.

Two plants can also report different OEE for the same machine. Whether changeovers sit inside planned time and how the ideal cycle time is set both move the result, as the [mistakes that change OEE](/blog/how-to-calculate-oee/#mistakes) show. For the same reason, OEE compared across dissimilar processes, or against an outside benchmark, says little.[2]

## Why do job shops and high-volume lines score differently?

A job shop and a high-volume line lose time to different things, so the same OEE asks for different work. A *job shop* makes many part numbers in short runs. A *high-volume line* runs one product, or a few, for days at a time.

Every job change in a job shop is a setup, and setup time counts against availability. A line making 10 different products loses far more availability to changeovers than a line making one.[2] Ljungberg also expected OEE to be lower when a company is new to a production process,[3] and a job shop meets a new process with each new part number.

#### Worked example: Setups alone on a job-shop mill

- For example, a Haas VF-2 runs five 450-minute shifts a week: 2,250 minutes of planned production time.
- It runs eight jobs a week, with a 40-minute setup each: 320 minutes.

Availability can’t exceed (2,250 − 320) ÷ 2,250 = 85.8%, before a single breakdown or material wait. Check: 320 ÷ 2,250 is 14.2% lost, and 100% − 14.2% = 85.8%.

**Result:** OEE ceiling = (1,930 ÷ 2,250) × 0.95 × 0.999 = 81.4%, even at world-class speed and quality

The same 40-minute changeover once a week on a high-volume bottling line costs 1.8% of the same 2,250 minutes. That line loses its time elsewhere: jams, short stops and slow running, which land in performance. Ljungberg found performance losses the dominant category across his cases,[3] and the Pladis line shows that a high-volume line can start as low as 20–30%.[5]

| | Job shop | High-volume line |
|---|---|---|
| Runs | Many part numbers, short runs | One or a few products, long runs |
| Setups | Several a day | A few a week |
| Where losses land first | Availability, from setups | Performance, from short stops |
| Ideal cycle time | New for each new part | Settled and well known |
| Where the goal comes from | The machine’s own baseline | The machine’s own baseline |

Loading sets a ceiling too. The [utilization ranges by machine type](/blog/utilization-goals-by-machine-type/#ranges) put a manual CNC at 40 to 60% uptime and a fully automatic one at 75 to 85%. Uptime is availability, so a manual job-shop CNC sits far below Nakajima’s 90% before speed or quality count.

## How do you set a goal from your baseline?

Set the first goal 5 to 10 points above the machine’s current average, and pick the end of that range from its trend. The [monitoring rollout plan](/docs/get-started/roll-out-plan/#phase-1) uses the same rule for a first uptime goal: a line at 55% gets a goal of 60%.

The *baseline* is the machine’s average over the last full quarter, measured with the same shifts and the same rule for planned time. The *trend* is the direction of its monthly figures over the last six months.

1. **Measure a full quarter.** Collect OEE and its three factors for one complete quarter, with the same definition of planned production time throughout.
2. **Read the trend.** Plot the last six monthly figures and decide whether they are flat, rising or falling.
3. **Set the goal.** Add 5 points when the trend is flat and 10 when it is rising. When it is falling, find what changed before you set any goal.
4. **Hold it for a quarter.** Leave the goal alone, then raise it once the machine meets it. A stretch target should be achievable within three or four months.[2]

#### Worked example: One CNC lathe’s goal from its trend

- For example, a CNC lathe reads 47%, 49%, 50%, 52%, 54% and 56% OEE over six months.
- Baseline: the last quarter averages (52 + 54 + 56) ÷ 3 = 54.0%.
- Trend: the figure rose 56 − 47 = 9 points over five monthly steps, 1.8 points a month. Check: 2 + 1 + 2 + 2 + 2 = 9.
- At that pace the lathe reaches 56 + 3 × 1.8 = 61.4% by the end of next quarter with no new project. That already passes the low end of 54 + 5 = 59%.

**Result:** Goal = 54 + 10 = 64%, the top of the 59–64% range

Figure: Monthly OEE on a CNC lathe rises from 47% to 56% over six months. The last quarter averages 54%, the trend alone reaches 61.4% next quarter, so the goal is set at 64%, the top of a 59–64% range.

*A rising trend would pass the low end on its own, so the goal goes to the top of the range. Sample data.*

> **When is 85% the right goal?**
>
> Use 85% as a goal only when a machine’s baseline already sits within 5 to 10 points of it, at 75 to 80%. Below that, 85% is a goal the machine misses every shift, and a red number nobody can turn green stops being read.

## Set OEE goals per machine

IoTFlows keeps one OEE goal for the organization and lets you override it on a single machine, so a manual job-shop mill and an automatic cell each get a goal from their own history. The goal colors the Uptime gauge and the parts gauge green at or above it and red below.

A SenseAi on each machine measures uptime and counts parts with no PLC connection, and IoTFlows reports [OEE factor by factor](/solutions/oee-tracking/). To find a baseline and its trend, open the [Advanced Report](/docs/monitoring/advanced-report/#read) over the last quarter at a weekly interval: the bars show the trend, and each machine’s row gives its own figure. The guide to [per-machine goals](/docs/monitoring/oee-goals/#machine) shows when an override is worth setting.

## Which factor should you chase first?

Chase the factor that loses the most minutes on the machine that limits output. In a job shop that is often availability, through setups; on a high-volume line it is often performance, through short stops and slow running.

A factor’s percentage sits on a different base of time from the next one’s, so convert each into lost minutes before you compare them. The guide to [which OEE factor loses the most](/blog/which-oee-factor-loses-most/) works that conversion through a week on a mill.

| Situation | Chase first | First check |
|---|---|---|
| Job shop, several setups a day | Availability | Setup minutes per job |
| High-volume line, few changeovers | Performance | Stops under the logging cutoff |
| Rejects waste costly material | Quality | Scrap log against the scrap bin |
| Performance reads over 100% | None yet | The ideal cycle time |
| Less than a quarter of data | None yet | Measure a full quarter |

Each row assumes the machine limits output. On a machine with spare capacity, start with quality, since scrap is the loss that costs cash there.

Choose availability when setups lead, and [measure changeover time](/blog/measure-changeover-time/) per job before you start a setup project. Choose performance when the machine runs most of the shift but output still lags its ideal. You don’t need a new OEE goal to start either one: the factor goal you set from the baseline tells you when the project worked.

## Questions

### Is 60% the average OEE for manufacturers?

No survey behind that number appears on the pages that repeat it. The closest published measurement is a 1998 study that found about 55% across roughly 20 cases. Treat 60% as a rule of thumb, and compare each machine with its own history.

### Is 85% OEE realistic for a job shop?

Rarely on a machine that changes jobs every day. Setups count against availability, so a mill with eight 40-minute setups in a 2,250-minute week tops out near 81% OEE even at 95% performance and 99.9% quality. Set the goal from the machine’s own baseline, and cut setup time to raise the ceiling.

### Should availability, performance and quality each have their own goal?

Track all three, and put a goal on the factor you are working on this quarter. The OEE goal tells you whether the machine improved, while the factor goals tell you why. Set each factor goal the same way as the OEE goal, a few points above that factor’s own baseline.

### How often should you raise an OEE goal?

Hold a goal for a quarter, then raise it once the machine meets it. A goal that changes every month never shows a trend, and a goal the machine has never met teaches operators to stop reading it. Raise it in the same 5 to 10 point steps you used to set it.

## Sources

1. [Anatomization of the overall equipment effectiveness (OEE) for various machines in a tool and die shop (Journal of Intelligent Manufacturing and Special Equipment 3(1), 2022)](https://www.emerald.com/jimse/article/3/1/97/433186/Anatomization-of-the-overall-equipment), S. Raju, H. A. Kamble, R. Srinivasaiah and D. R. Swamy, Emerald Publishing, accessed Oct 30, 2026
2. [World-Class OEE: Set Targets To Drive Improvement](https://www.oee.com/world-class-oee/), Vorne Industries, accessed Oct 30, 2026
3. [Measurement of overall equipment effectiveness as a basis for TPM activities (International Journal of Operations & Production Management 18(5), 1998)](https://www.emerald.com/insight/content/doi/10.1108/01443579810206334/full/html), Õrjan Ljungberg, Emerald Publishing, accessed Oct 30, 2026
4. [Total Productive Maintenance Reduces Equipment Downtime and Lost Capacity](https://www.nist.gov/mep/successstories/2022/total-productive-maintenance-reduces-equipment-downtime-and-lost-capacity), NIST Manufacturing Extension Partnership, accessed Oct 30, 2026
5. [Taking Action Against Productivity Losses](https://www.nist.gov/mep/successstories/2022/taking-action-against-productivity-losses), NIST Manufacturing Extension Partnership, accessed Oct 30, 2026