What is a good OEE?
A good OEE depends on the process. The figures most often quoted are 85% as world class for discrete manufacturing, 60% as typical, and 40% as common for a plant measuring properly for the first time. The more useful target is your own line's best shift, because it was achieved with that equipment, that product and that crew.
The short answer
If you are asking because you have just measured OEE for the first time and the number is in the 40s or 50s, that is normal, and it is not a verdict on the plant. It is the first honest look at where planned production time goes. If you are asking because you want a target, read the next three sections before adopting 85%.
The benchmarks
The benchmark tiers quoted almost everywhere trace back to Vorne's OEE reference material.
| OEE | How it is usually described | What it implies |
|---|---|---|
| 100% | Perfect production | Only good units, at ideal speed, with no stops. A theoretical ceiling. |
| 85% | World class, discrete manufacturing | Often quoted as 90% Availability × 95% Performance × 99.9% Quality. |
| 60% | Typical | Substantial room to improve, usually in Availability. |
| 40% | Common for a first measurement | Most of the loss has never been seen, let alone owned. |
Source: Vorne, oee.com (external benchmark tiers); the 90 × 95 × 99.9 decomposition is the commonly cited composition of 85%
Why they mislead
Three reasons a single benchmark cannot tell you whether your number is good.
Process type. A continuous process with few changeovers and one product can run at an OEE that a packaging line changing SKUs four times a shift will never see, with both plants managed well. The benchmark does not know which you are.
Definitions. Two plants reporting 65% may have calculated it differently: one counts planned cleaning as downtime and the other excludes it; one sets Ideal Run Rate at the nameplate and the other at what the line usually does. Until the formula and the inputs match, the numbers are not comparable. How to calculate OEE sets out one formula.
Measurement method. An OEE built from manual logs misses the interruptions (what standard OEE calls minor stops or micro-stops) that are too short to write down, and so reports a higher Availability and a lower Performance than the line actually had. A measured OEE, from a sensor counting cycles and a terminal capturing stops, is often lower at first and more useful thereafter.
Your own best shift
The target that survives all three objections is the one your own line has already achieved. Augos calls it the Best Operator Benchmark: take the best operator's response times for each stop cause, and the line's best plausible day can be synthesised from your own floor. It is an empirical maximum, not a textbook one. Across the Augos fleet, the average gap between a line's current OEE and its Best Operator Benchmark is about 18 OEE points.
That gap is the point of measuring. It is capacity already proven on that line, with no capital spent, and the Owner column says which function holds each part of it.
Source: Augos Glossary; Augos Factory method
Direction beats level
A good OEE programme is one where the number is measured the same way every shift, the loss is broken down by owner, and the trend is up. A plant at 55% that knows its 20 largest stops by cause and owner is in a better position than one at 70% that reports a single percentage at the end of the month. Standard OEE shows the loss. OEE+ shows who owns it.
Common questions
What is a good OEE score?
It depends on the process. 85% is widely quoted as world class for discrete manufacturing, 60% as typical and 40% as common for a plant measuring properly for the first time. A line's own best shift is a better target than any of them.
What is world class OEE?
85% is the figure most often called world class, usually composed as 90% Availability, 95% Performance and 99.9% Quality. It comes from Vorne's OEE reference material and describes discrete manufacturing; continuous processes and high-changeover lines sit on different scales.
Is 60% OEE bad?
No. It is the figure most often described as typical, and for a first measurement it is common to see less. What matters is whether the 40% that was lost is broken down by cause and owner, so that it can be recovered.
Why did our OEE drop when we started measuring it automatically?
Because measured OEE catches the stops and slow running that manual logs miss. The line did not get worse; the number got honest. The recoverable gap is now visible.
Should I set 85% as our target?
Only if your process resembles the discrete manufacturing the benchmark describes and your formula matches. A safer target is your own line's best shift, which you know is achievable with the equipment and people you have.