What is OEE?
Overall Equipment Effectiveness (OEE) is the single most widely used measure of manufacturing productivity. It answers one question: of the time you planned to produce good parts, how much did you actually spend producing good parts at full speed? A score of 100% means you made only good parts, as fast as the equipment can run, with no stop time — perfect production.
OEE is the product of three factors, each scored from 0–100%:
- Availability — the share of planned production time the line was actually running (lost to breakdowns, changeovers, and unplanned stops).
- Performance — how close the line ran to its ideal cycle time while running (lost to minor stops and reduced speed).
- Quality — the share of produced parts that were good first time (lost to scrap, rework, and startup rejects).
Because the factors multiply, OEE is unforgiving: a line that is 90% available, runs at 90% of rate, and yields 90% good parts scores just 73% (0.9 × 0.9 × 0.9), not 90%. That is the point — OEE surfaces compounding losses a single metric would hide.
What is TEEP — and how is it different?
OEE measures effectiveness against the time you scheduled. Total Effective Equipment Performance (TEEP) measures it against all the time there is — every hour on the calendar. It adds a fourth factor, Utilization:
Where OEE asks “how well did we run when we were scheduled to run?”, TEEP asks “how much of our total capacity are we actually using?” A plant running a single 8-hour shift can have excellent OEE and poor TEEP — the equipment sits idle two-thirds of the day. TEEP is the metric for capacity and capital-expansion decisions; OEE is the metric for shop-floor improvement.
How to calculate OEE (worked example)
Take a shift with 480 planned minutes. The line is down 47 minutes (breakdown + changeover), so it runs 433 minutes. It produces 14,400 parts at an ideal rate of 40 parts/minute, and 220 of those parts are defective.
| Factor | Calculation | Result |
|---|---|---|
| Availability | 433 run ÷ 480 planned | 90.2% |
| Performance | 14,400 parts ÷ (433 min × 40/min) | 83.1% |
| Quality | (14,400 − 220) ÷ 14,400 | 98.5% |
| OEE | 0.902 × 0.831 × 0.985 | 73.8% |
The three factors also tell you where to look: availability and performance are dragging this line, not quality. That diagnosis — not the headline number — is where the money is.
What is a good OEE score?
The figures below are the benchmarks most commonly cited across discrete manufacturing. Treat them as orientation, not gospel — “good” depends heavily on process, product mix, and how honestly you define planned time.
| Score | Interpretation |
|---|---|
| ~85% | World-class for discrete manufacturers (≈ 90% availability × 95% performance × 99.9% quality). |
| ~60% | Typical for manufacturers not yet measuring OEE systematically — substantial room to improve. |
| ~40% or below | Common for plants just starting out; usually low-hanging fruit, not a crisis. |
The Six Big Losses
OEE is most useful when you decompose it into the Six Big Losses — the standard taxonomy that maps every lost minute or part to one of the three factors. Fix the loss, and the factor (and OEE) moves.
| Loss | OEE factor | Example |
|---|---|---|
| Breakdowns | Availability | Unplanned equipment failure or stoppage |
| Setup & adjustments | Availability | Changeovers, material changes, warm-up |
| Idling & minor stops | Performance | Jams, misfeeds, sensor faults, blocked flow |
| Reduced speed | Performance | Running below ideal cycle time, wear, derating |
| Process defects | Quality | Scrap and rework during stable production |
| Startup rejects | Quality | Defects during warm-up or after a changeover |
From measurement to verified savings
Measuring OEE is the easy part. The hard part — and the part that pays — is turning each loss into a ranked, owned action and then proving the savings actually landed. Most programs stall here: opportunities are guessed, savings are estimated on a slide, and the number never reconciles with the P&L.
That gap is exactly what KaizenFlow is built to close. It connects to the systems already producing your OEE data (MES, SCADA, ERP, historians), uses an AI ensemble to rank every loss by dollar impact and confidence, and then reconciles the result against a normalized baseline — so a recommendation becomes a verified, finance-signed figure rather than a projection.