If you could track just one number on the shop floor, OEE would be a strong candidate. It compresses availability, speed and quality into a single, honest percentage — and it tells you, at a glance, how much of your production potential you're actually using.
This guide explains what OEE is, the exact formula, a fully worked example, what a good score looks like, and how to go from a one-off calculation to live tracking.
What is OEE?
OEE (Overall Equipment Effectiveness) is the percentage of planned production time that is truly productive — that is, producing good parts at the rated speed. An OEE of 100% means you make only good parts, as fast as theoretically possible, with no stop time. Nobody hits 100%, but the closer you get, the less hidden capacity you're leaving on the table.
Its power is that it combines three things that are usually measured separately, so it can't be gamed by being great at one and quietly losing on another.
The three factors
OEE is the product of three rates, each between 0 and 100%:
- Availability — how much of the planned time the machine was actually running.
Availability = Run Time ÷ Planned Production Time. It is hurt by breakdowns, changeovers and unplanned stops. - Performance — how fast it ran while it was running, versus its ideal speed.
Performance = (Ideal Cycle Time × Total Count) ÷ Run Time. It is hurt by minor stops and reduced speed. - Quality — how many of the parts produced were good.
Quality = Good Count ÷ Total Count. It is hurt by scrap and rework.
The OEE formula
OEE = Availability × Performance × Quality
Because the three are multiplied, a weak link drags the whole score down — which is exactly the point. A machine that's "up" 90% of the time but runs slow and makes scrap is not a 90% machine.
A worked example
Take one 8-hour shift on a single machine:
| Step | Value |
|---|---|
| Planned production time (480 min − 30 min breaks) | 450 min |
| Stop time (breakdown + changeover) | 50 min |
| Run time (450 − 50) | 400 min |
| Ideal cycle time | 1.0 min / part |
| Total parts produced | 350 |
| Good parts (10 rejected) | 340 |
- Availability = 400 ÷ 450 = 88.9%
- Performance = (1.0 × 350) ÷ 400 = 87.5%
- Quality = 340 ÷ 350 = 97.1%
OEE = 0.889 × 0.875 × 0.971 = 75.5%. In other words, roughly a quarter of this machine's potential output was lost — and OEE shows you it was split across stop time, speed and scrap.
What's a good OEE?
- 85% is considered world-class for discrete manufacturing (roughly 90% Availability × 95% Performance × 99.9% Quality).
- ~60% is typical for a plant that hasn't focused on it — which means a large, recoverable opportunity.
- 40% or below is common when there's no measurement at all.
The exact target matters less than the trend. A rising OEE, tracked consistently, is the sign of a plant that's improving.
The Six Big Losses
Everything that pulls OEE below 100% falls into six well-known categories — and each maps to one of the three factors:
- Availability: breakdowns; setup & changeovers.
- Performance: idling & minor stops; reduced speed.
- Quality: process defects; reduced-yield startup.
Categorising losses this way turns a vague "we should do better" into a ranked list of specific things to fix.
From formula to live tracking
Calculating OEE once, by hand, is a useful exercise. Doing it every shift, for every machine, by hand, is not. The value comes from tracking it continuously — so you see the trend, spot the worst losses and act on them.
That's what Production Tracker does inside Power BI: it computes per-machine OEE — Availability, Performance and Quality — from the data your shop floor already records, with the planned/unplanned downtime split built in, and no DAX required. The formula above stops being a spreadsheet exercise and becomes a live number on the wall.
The bottom line
OEE is one honest percentage that answers a hard question: how much of your capacity are you really using? Learn the formula, calculate it once to internalise it — then track it continuously, because the trend is where the improvement lives.