OEE Calculation: Formula and a Fully Worked Example

Here is the formula in full, then a shift's worth of real numbers run through it, because the formula alone tells you almost nothing about where a plant actually loses points.

The three ratios

• Availability = Run Time ÷ Planned Production Time. Planned Production Time is the shift minus scheduled non-production time (breaks, planned maintenance). Run Time is what's left after subtracting unplanned downtime — breakdowns, jams, material waits.
• Performance = (Ideal Cycle Time × Total Count) ÷ Run Time. Ideal Cycle Time is the fastest sustainable time to make one part on that machine, not the vendor's nameplate speed. Total Count is everything produced, good or bad.
• Quality = Good Count ÷ Total Count. Good Count is parts that pass on the first attempt — reworked parts don't count twice.

Multiply the three and you get OEE. For the meaning behind each ratio and what a good score looks like, see what is OEE.

A full worked shift

Take an 8-hour shift on a packaging line, Planned Production Time of 440 minutes after 40 minutes of scheduled breaks.

During the shift, three unplanned stops eat into Run Time: a 15-minute changeover overrun, a 25-minute infeed jam, and a 20-minute wait on incoming material — 60 minutes of downtime.

• Run Time = 440 − 60 = 380 minutes
• Availability = 380 ÷ 440 = 86.4%

The line's validated Ideal Cycle Time — the fastest rate it holds on a good run, not the OEM's spec sheet — is 1.2 seconds per unit. Over the shift it actually produces 16,500 units.

• Time those 16,500 units would take at ideal speed: 16,500 × 1.2 sec = 19,800 sec = 330 minutes
• Performance = 330 ÷ 380 = 86.8%

Of those 16,500 units, 500 are scrapped or reworked at final inspection, leaving 16,000 good units on the first pass.

• Quality = 16,000 ÷ 16,500 = 97.0%

• OEE = 86.4% × 86.8% × 97.0% = 72.7%

Three individually respectable numbers — mid-80s Availability, mid-80s Performance, high-90s Quality — compound down to a score in the low 70s. That's the multiplicative effect what is OEE covers in more depth, and it's the reason OEE catches losses that no single department's report would flag on its own.

INX International is a real version of that math moving: a 21.4% OEE increase at a chemical manufacturer, driven mostly by a 20.5% Performance-factor gain once ideal cycle times were measured and tracked against, rather than assumed.

Three ways the calculation breaks

Counting planned downtime as availability loss, inconsistently. Whether a changeover, a scheduled clean, or a no-order gap belongs in Planned Production Time or counts as an Availability loss is a decision, not a fact — and it has to be made the same way every shift. Move the boundary between "planned" and "loss" mid-quarter and last month's OEE stops being comparable to this month's, even though nothing on the floor changed.

Using nameplate speed instead of a validated Ideal Cycle Time. A machine's rated speed is a vendor's theoretical maximum under conditions the floor never actually sees. Plug it in as Ideal Cycle Time and Performance drops to reflect a rate nobody could sustain, which makes the whole OEE score too low to act on and too easy to dismiss. The correct number is the best sustained rate the line has actually held for that part, validated by a time study, not read off a spec sheet.

Counting reworked parts as good. A unit that failed inspection, got pulled, reworked and passed on the second attempt consumed real cycle time twice, but Good Count should only ever reflect the first pass. Count it as good and Quality — and therefore OEE — reports a plant that's healthier than the one on the floor, hiding exactly the rework loop worth fixing.

Where to go next

• For what the score means once you have it — including why "world class 85%" isn't a target to chase blindly — see what is OEE.
• For what a downtime minute actually costs beyond its Availability impact, see machine downtime.
• The process optimization benchmarks hold the full range and sample sizes behind the 25% median OEE/productivity gain cited above, and the use-case hub lists every deployment behind it.
• Zanini Renk and Fonterra show the same calculation applied at very different starting baselines.