OEE (overall equipment effectiveness) = availability × performance × quality. Availability is run time ÷ planned production time. Performance is (ideal cycle time × total count) ÷ run time. Quality is good count ÷ total count. Multiply the three and you get the share of planned time spent making good parts at full speed. A quick check: OEE also equals (good count × ideal cycle time) ÷ planned production time. The steps below work through a shift, and the Example gives 77.3%.
The three factors map to the six big losses described by Seiichi Nakajima in his work on total productive maintenance. Availability captures breakdowns and setups or changeovers. Performance captures minor stops and reduced speed. Quality captures startup rejects and production rejects. Splitting OEE this way tells you where to look: a line with high quality and low availability needs maintenance and changeover work, not more inspection.
OEE describes one machine or line against its own potential. It is not a capacity plan and not a comparison between different machines. For demand and staffing, use takt time; for cycle analysis, the cycle time calculator. The lean manufacturing documentation guide, the pillar for this cluster, shows where OEE fits in a lean system.
The ideal cycle time is the single choice that most changes the result. Use the design rate of the machine or the best rate the process has sustained, not the standard used for costing, which usually includes allowances. If different parts run on the same machine, use each part’s own ideal cycle time and sum the ideal minutes across parts.
Write the chosen value down and keep it fixed. Changing the ideal cycle time to make OEE look better hides the speed loss you are trying to find.
OEE uses planned production time as its base. TEEP (total effective equipment performance) uses all calendar time, so it also shows time the equipment was not scheduled. Published benchmarks exist, but they depend heavily on industry and on how each plant defines planned time and ideal cycle time. Compare a line with its own history first. The glossary entry for OEE gives a short definition to share with your team.
OEE is only as good as the stop and count data behind it. At minimum you need, per shift: planned production time, every stop longer than a threshold you set (for example two minutes) with a reason code, the total count and the reject count. A paper sheet at the machine works; so does a counter on the machine with a tablet for stop reasons. Keep the reason codes short, around ten, so operators pick them consistently, and include an “other” code that someone reviews weekly.
Short stops under the threshold are not logged one by one. They show up as a performance loss instead, which is correct: the line ran, just slower than ideal. Review the data at the start of each shift with the team, look at the largest loss from the previous shift, and pick one action. The equipment maintenance log is a natural home for breakdown follow-ups, so availability losses turn into maintenance work orders rather than just numbers on a chart.
Example: OEE for one shift on a fictional CNC cell
Result: Example result: OEE is 77.3%. Of the 153 parts lost, 48 min of stops account for 48 × 60 ÷ 40 = 72 parts, slow running for (402 − 360) × 60 ÷ 40 = 63 parts and rejects for 18 parts. The changeover is the biggest single loss. All values are example values.
OEE = availability × performance × quality, which simplifies to (good count × ideal cycle time) ÷ planned production time.
No. Planned breaks are removed before OEE is calculated. Only unplanned stops and changeovers reduce availability.
Not if the data is right. A result over 100% usually means the ideal cycle time is set too slow or the counts are wrong.
It depends on the process and how you define the inputs. Improve against your own baseline and compare lines only when they use the same definitions.
OEE measures against planned production time. TEEP measures against all calendar time, so it also captures unscheduled time.
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