OEE Formula Explained: Availability × Performance × Quality With a Worked Example

By Dzulfikar Ats Tsauri, founder of Voltrus / JoyoDigitama. He builds the protocol tools covered in these posts.

Overall Equipment Effectiveness (OEE) is one number that tells you how well a machine actually runs. It is not a mysterious KPI: it is three measured percentages multiplied together. This guide walks through each component, runs a full worked example with real numbers, and shows the common mistakes that make scores look better than the floor really is.

The Formula

OEE = Availability × Performance × Quality

Each factor is a ratio between 0 and 1 (0–100%), so OEE is also a percentage. The math deliberately punishes weakness: if any one factor drops, the total drops with it, and compound losses are visible immediately.

Availability: Was the Machine Scheduled to Run and Did It?

Availability = Operating Time / Planned Production Time

• Planned production time is the shift time minus time you never intended to run (typically breaks).
• Operating time is planned production time minus stop time: unplanned stops such as breakdowns and jams and, in the usual OEE convention, planned stops such as changeovers.

Availability answers: of the time we expected the machine to produce, how much did it actually run?

Performance: Did It Run at the Expected Speed?

Performance = (Ideal Cycle Time × Total Units) / Operating Time

Performance compares actual output against what the machine should have produced at ideal cycle time during its operating time. It captures:

• Slow cycles: the machine runs, but slower than the ideal rate
• Small stops: brief stoppages too short for anyone to log a reason (usually under a few minutes) that never get logged as downtime
• Minor jams and adjustments: small losses that add up across a shift

This is the component most plants under-measure, because micro-stops and slow running rarely appear in downtime logs.

Quality: Of What You Made, How Much Was Good?

Quality = Good Units / Total Units

Quality counts defects, rework, and scrap against total production. Units that need rework are not first-pass good, so they do not count as good units. Quality is a first-pass metric, not a final-shipment metric.

A Full Worked Example

Take a packaging machine with an ideal cycle time of 1 second per unit (60 units per minute). One 8-hour shift:

Input	Value
Shift length	480 minutes (8 h)
Planned breaks	60 minutes
Unplanned downtime	45 minutes (jam + fault)
Total units produced	18,000
Good units	17,100 (900 scrap)

Step 1: Availability:

Planned production time = 480 − 60 = 420 min
Operating time           = 420 − 45 = 375 min
Availability             = 375 / 420 = 0.893 → 89.3%

Step 2: Performance:

Ideal cycle time × total units = 1 s × 18,000 = 18,000 s = 300 min
Performance                    = 300 / 375 = 0.80 → 80.0%

The machine was down for 45 minutes, but it also lost 75 minutes of the remaining 375 to micro-stops and slow cycles; that is the performance loss, invisible in any downtime log.

Step 3: Quality:

Quality = 17,100 / 18,000 = 0.95 → 95.0%

Step 4: OEE:

OEE = 0.893 × 0.80 × 0.95 = 0.679 → 67.9%

An OEE of 67.9% is respectable but unremarkable, and it shows exactly where to look. Availability is fine. Performance is the problem: 75 minutes of unlogged micro-loss. That is the actionable insight OEE exists to surface.

What the Numbers Mean

OEE is not a passing grade; it is a diagnosis. The usual reference points are:

• 100%: perfect production: no downtime, at ideal speed, zero defects. An aspiration, not a target.
• 85%: the "world-class" figure for discrete manufacturing, which comes from the TPM work of Seiichi Nakajima (90% Availability × 95% Performance × 99% Quality) and is a minimum he suggested companies strive for. It is based on one place, era and industry (Japan, 1970s, automotive).
• About 60%: where many manufacturers are in practice, according to oee.com's world-class OEE page, which also says it sees more companies below 45% than above 85%.
• Below 40%: large, obvious losses; often a data problem as much as a machine problem.

The benchmark matters less than the trend. The real power of OEE is watching each component move as you fix the underlying losses.

The Mistakes That Inflate Your Score

2. Only counting logged downtime. If micro-stops and slow cycles are not measured, Performance silently reads 100% and your OEE looks great while the floor visibly underproduces. Measuring performance requires counting units, not just logging faults.

3. Setting the ideal cycle time too slow. Ideal cycle time should be the engineered design rate, not the average you currently achieve. A slower-than-design ideal cycle time flatters Performance; a faster one deflates it.

4. Counting rework as good. First-pass quality is the honest metric. A part reworked twice still cost you capacity.

Getting the Data Without a Spreadsheet

The math is easy; the data is hard. Availability needs accurate downtime classification, performance needs unit counts and cycle times, quality needs first-pass good counts. Collecting that by hand with a clipboard and a stopwatch is tedious, and manual OEE tracking tends to fade after a few weeks.

Voltrus OEE Line computes this for you: machine states (running, idle, down) with reason codes marked planned or unplanned, plus good and scrap counts, feed OEE, Availability, Performance and Quality per machine and as a weighted roll-up for the line, within shift time, without anyone transcribing anything.

Keep reading

Guide OEE Formula Explained with a Worked Example August 16, 2026 Guide Manufacturing Downtime Tracking: Measure, Classify, Reduce Stops August 16, 2026