OEE — overall equipment effectiveness — is the share of planned production time in which a machine or line makes good parts at its ideal speed. It is the product of three factors: availability (was it running?), performance (at full speed?) and quality (were the parts good?). It was introduced by Seiichi Nakajima as the core measure of Total Productive Maintenance, and ISO 22400-2 lists it among its manufacturing KPIs.
OEE = availability × performance × quality, or in one line: good count × ideal cycle time ÷ planned production time. In the worked example below that is 74.7%, and every step can be checked. Changeovers are an availability loss, not planned downtime. And when you combine shifts or machines, calculate from summed minutes and counts — never average the percentages: in this page’s example the average says 56.1% and the truth is 66.8%.
Download the OEE template
The Excel file does the arithmetic: enter minutes and counts per shift and the four percentages calculate themselves, with a total row built from sums rather than averages. A loss log assigns each of the six big losses to its factor automatically. The Word and PDF versions are a printable shift sheet for lines where data is still collected on paper. No signup, no email.
OEE template — Excel (.xlsx)
Live formulas, twenty shift rows, a pooled total row, a loss log with a drop-down of the six big losses, and a formula sheet.
OEE shift sheet — Word (.docx)
Time, output and result on one page, plus a loss log and the six-losses reference to fill in at the line.
OEE shift sheet — PDF (print)
Two printable pages — the sheet on the clipboard at the machine, and the reference on the back.
The OEE formula
Every term depends on the one before it, so the order matters. Start from the clock, not from the counts.
| Term | Formula | What it answers |
|---|---|---|
| Planned production time | Shift length − breaks and time with no demand | How long could we have produced? |
| Run time | Planned production time − all stops, including changeovers | How long did we actually produce? |
| Availability | Run time ÷ planned production time | Was it running when it should have been? |
| Performance | (Ideal cycle time × total count) ÷ run time | While running, was it at full speed? |
| Quality | Good count ÷ total count | Of what it made, how much was right first time? |
| OEE | Availability × performance × quality | What share of the possible good output did we get? |
The one-line check. OEE also equals good count × ideal cycle time ÷ planned production time, because the intermediate terms cancel out. Calculate it both ways; if the two results differ, one of your inputs is wrong.
A worked example you can check
Illustrative figures, not a real line. One eight-hour shift on a machine whose ideal cycle time is 30 seconds per part.
| Step | Calculation | Result |
|---|---|---|
| Planned production time | 480 min shift − 30 min break | 450 min |
| Stops | 25 min changeover + 17 min breakdown | 42 min |
| Run time | 450 − 42 | 408 min |
| Availability | 408 ÷ 450 | 90.7% |
| Ideal output in run time | 408 min × 2 parts per minute | 816 parts |
| Performance | 700 parts made ÷ 816 | 85.8% |
| Quality | 672 good ÷ 700 made | 96.0% |
| OEE | 0.907 × 0.858 × 0.960 | 74.7% |
| One-line check | 672 × 0.5 min ÷ 450 min | 74.7% |
Where the 450 minutes went. 336 minutes of fully productive time — good parts at ideal speed. The other 114 minutes split three ways: 42 lost to stops (availability), 58 to running slow or stopping briefly (performance), and 14 spent making the 28 bad parts (quality). That split is more useful than the percentage: it says the biggest loss on this shift was speed, not breakdowns — which is rarely where a team would have guessed.
The six big losses
Every minute of lost time belongs to exactly one of six losses, and every loss belongs to exactly one factor. That is what makes OEE traceable: a low number can always be taken apart.
| Loss | Factor | Typical examples | What to record |
|---|---|---|---|
| Equipment failure | Availability | Breakdowns, unplanned maintenance, tooling failure | Minutes stopped, and the reason |
| Setup and adjustments | Availability | Changeovers, warm-up, first-off approval, material shortage | Last good part to first good part |
| Idling and minor stops | Performance | Jams, starved or blocked sensors, stops cleared by the operator | Count of stops under a threshold, e.g. 5 min |
| Reduced speed | Performance | Cycle slower than ideal: worn tooling, cautious settings, material | Actual cycle time against ideal |
| Process defects | Quality | Scrap and rework during stable production | Parts, with the defect type |
| Startup rejects | Quality | Scrap and rework after a changeover or restart | Parts, kept separate from process defects |
Minor stops are the loss most often invisible in the data, because nobody logs a 40-second jam. They show up only as a gap between run time and ideal output — which is why performance is usually the lowest of the three factors on a line that has never measured it.
Five ways OEE numbers lie
1. Averaging percentages across shifts or machines. Two shifts on the same line: the example above at 74.7%, and a short night shift of 120 planned minutes that lost half of them to a breakdown, at 37.5%. The average is 56.1%. The correct figure, from summed minutes and counts, is 66.8% — because the good shift was almost four times longer. Averages give every row the same weight whatever its size; the template’s total row is built from sums for exactly this reason.
2. Treating changeovers as planned downtime. Removing changeovers from planned production time raises availability without a single minute being saved. Setup and adjustments is one of the six big losses; it belongs inside the calculation, or SMED work has nothing to show for itself.
3. Using nameplate speed instead of ideal cycle time. A machine rated at 3,000 parts an hour that has never sustained more than 2,400 will report a permanent performance loss nobody can close. Use the fastest cycle the process has demonstrated it can hold with good quality — and write down where the number came from.
4. Counting rework as good. Quality in OEE means right first time. A part that went back through the process consumed capacity twice; counting it as good hides both the defect and the lost time.
5. Comparing OEE between different machines. A single-product line and a high-mix line with daily changeovers are not doing the same job, and 70% on one says nothing about 70% on the other. The comparison that means something is the same line against its own history — which is also the only comparison a target should be based on.
From OEE to root cause
OEE is a measurement, not an analysis. It tells you where the time went, never why. The step that turns it into improvement is short:
- Log every loss with a reason, not just its minutes — the template’s loss log does this and assigns the factor automatically.
- Rank the losses with a Pareto chart, by minutes rather than by count. The loss that happens most often is rarely the one that costs the most time.
- Take the largest one into a root cause session — 5 Whys if the chain is short, a fishbone first if it is contested.
- Put the fix into the control plan so the loss cannot drift back, and measure OEE on the same line to see whether it moved. The control plan is where that happens.
Frequently asked questions
What is OEE?
OEE — overall equipment effectiveness — is the share of planned production time in which a machine or line makes good parts at its ideal speed. It is the product of three factors: availability (was it running?), performance (was it running at full speed?) and quality (were the parts good?). It was introduced by Seiichi Nakajima as the core measure of Total Productive Maintenance, and ISO 22400-2 lists it among its manufacturing KPIs.
How do you calculate OEE?
Multiply three factors. Availability is run time divided by planned production time. Performance is ideal cycle time multiplied by total count, divided by run time. Quality is good count divided by total count. OEE is availability × performance × quality. The same result comes from one line: good count × ideal cycle time ÷ planned production time — a useful check on the three-factor calculation.
What is a good OEE score?
85% is the figure usually called world class, from roughly 90% availability, 95% performance and 99.9% quality. It is a reference point, not a target to compare machines against: a changeover-heavy line and a single-product line with the same OEE are not equally good, and a score can be raised by moving losses out of planned production time. The useful comparison is the same line against itself, over time.
What are the six big losses in OEE?
Equipment failure and setup and adjustments reduce availability. Idling and minor stops and reduced speed reduce performance. Process defects and startup rejects reduce quality. Each loss belongs to exactly one factor, which is what lets an OEE figure be traced back to where the time went.
Are changeovers included in OEE?
Yes. A changeover happens inside planned production time and is an availability loss — setup and adjustments is one of the six big losses. Treating changeovers as planned downtime and removing them from planned production time inflates availability and hides the loss that SMED work is meant to reduce. Only breaks and time with no demand are excluded.
How do you calculate OEE for several shifts or machines?
From the summed minutes and counts, not by averaging the OEE percentages. Add up planned production time, run time, ideal output and good count across shifts, then calculate the three factors from the totals. Averaging percentages gives a short bad shift the same weight as a long good one: in the example on this page, an average gives 56.1% where the correct pooled figure is 66.8%.
What is the difference between OEE and TEEP?
OEE measures how well planned production time is used. TEEP — total effective equipment performance — measures how well all calendar time is used: it is OEE multiplied by utilization, the share of total time that is planned for production. OEE tells you how to run the shifts you have; TEEP tells you how much capacity is left in the shifts you do not run.
Is OEE a root cause analysis tool?
No. OEE tells you where the time went — which factor and which of the six losses — but not why. It is the measurement that decides where root cause analysis is worth doing: a Pareto of the losses picks the largest one, and a 5 Whys or fishbone on that loss finds the cause. An OEE number without a loss log is a score with no diagnosis.
Related resources
- Pareto chart template — rank the six losses by minutes, not by count
- 5 Whys manufacturing examples — including unplanned CNC downtime traced to its cause
- Kaizen event agenda — five days on the largest loss
- Control plan — where the fix is held once it works
- RCA meeting agenda — the hour you spend on the top loss
- Pareto chart maker — paste the loss log, get the chart