What this calculator does
Overall Equipment Effectiveness compresses a shift’s worth of production reality into one number, by asking a single question: of the time you planned to make product, how much of it actually produced good product at the machine’s own best rate? Everything else — every stop, every slow cycle, every reject — is a loss, and OEE is the fraction that survives.
Feed it seven figures off the shift record: total scheduled time, planned downtime, unplanned downtime, ideal cycle time, total units, defective units, and the calendar time for the period. It returns availability, performance and quality separately, their product as OEE, and — the part most OEE tools skip — TEEP, which measures the same productive time against wall-clock time rather than against the schedule you chose.
It also prices each factor’s loss in minutes, so the three numbers are directly comparable. “Availability 88.8%” and “performance 86.1%” look similar; “47 minutes lost to stops, 52 minutes lost to speed” tells you which meeting to call.
The formula
The Nakajima decomposition peels time back one layer at a time:
Planned Production Time PPT = Total Time − Planned Downtime
Run Time RT = PPT − Unplanned Downtime
Fully Productive Time FPT = Ideal Cycle Time × Good Count
Availability A = RT / PPT
Performance P = (Ideal Cycle Time × Total Count) / RT
Quality Q = Good Count / Total Count
OEE = A × P × Q
The three ratios telescope. Substitute and cancel and you get the identity worth remembering, because it is the check that your inputs are consistent:
OEE = (Ideal Cycle Time × Good Count) / Planned Production Time = FPT / PPT
TEEP (Total Effective Equipment Performance) keeps the same numerator and swaps the denominator for wall-clock time:
Utilisation U = PPT / Calendar Time
TEEP = OEE × U = FPT / Calendar Time
The equipment-effectiveness definitions follow ISO 22400-2, the ISO-style KPI set for manufacturing operations management; consult the current edition for the authoritative wording used in formal reporting. Nothing here claims compliance with it.
Reading the result
Planned versus unplanned downtime is the whole argument. Planned downtime is removed from the denominator before OEE is calculated, so anything you classify as planned is invisible to the number. Unplanned downtime sits inside planned production time and is charged straight to availability. The consequence is blunt: reclassify a two-hour changeover from unplanned to planned and OEE rises without a single thing improving on the floor.
The defensible line is intent, not cause. Planned downtime is time you decided in advance not to produce in — scheduled breaks, planned preventive maintenance, no demand, a shift you chose not to staff. Unplanned downtime is time you intended to produce in and did not: breakdowns, unplanned adjustments, material starvation, blockage, and — contentiously but correctly — changeovers, since setup and adjustment is one of the six big losses and belongs to availability. If your changeovers sit in the planned bucket, your OEE is not measuring what it claims to. Whatever line you draw, draw it once and never move it, because OEE is only useful as a trend.
The ideal cycle time must be the theoretical fastest, not the historical average. This is the second thing people get wrong, and it is the more damaging one. If you set the ideal cycle time to the rate you usually achieve, performance pins near 100% by construction and the entire speed-loss category — reduced speed and small stops, two of the six big losses — vanishes from your reporting. The correct value is the nameplate rate: the fastest cycle the machine has ever sustained, or the design rate from the OEM, for that product on that machine. If a different product runs slower, that is a different ideal cycle time, not a performance loss.
Performance above 100% is therefore not a triumph, it is a data error, and this calculator fails on it rather than reporting it. A machine cannot beat its own fastest possible cycle. The calculator tells you what cycle time the shift’s output actually implies, which is the number to go and check against the OEM sheet.
TEEP answers a different question than OEE. OEE grades how well you ran the time you scheduled; TEEP grades the asset against every hour it existed. A plant running one shift a day can hold 85% OEE and 28% TEEP simultaneously — both true, and they point at different decisions. OEE is an operations improvement metric, TEEP is a capital utilisation one.
Typical values
The commonly cited discrete-manufacturing benchmarks are 85% OEE as world class (roughly 90% availability, 95% performance, 99.9% quality), around 60% as typical, and 40% as low but common in plants that have not measured before. Treat all of these as orientation, not targets: they were derived from discrete assembly and travel badly to process, batch, or high-mix low-volume operations, where changeover-dominated schedules make 85% arithmetically unreachable.
More useful than the benchmark is the shape of the loss. A first honest measurement usually finds one factor carrying most of the loss, and it is most often performance — because small stops under a few minutes are rarely logged, and they surface as speed loss instead. If availability and quality look fine and performance is poor, the answer is usually not a slow machine; it is a stop log that only captures the long stops.
Worked example
One 8-hour shift. Two 15-minute breaks and a 30-minute meal break are planned downtime, and the stop log totals 47 minutes of unplanned stops. The machine’s ideal cycle time is 1.0 seconds per unit. It produced 19,271 units, of which 423 were rejects. Calendar time for the day is 24 hours.
PPT = 480 − 60 = 420 min
RT = 420 − 47 = 373 min
Good count = 19 271 − 423 = 18 848 units
A = 373 / 420 = 0.8881 → 88.81 %
P = (1.0 s × 19 271) / (373 × 60 s)
= 19 271 / 22 380 = 0.8611 → 86.11 %
Q = 18 848 / 19 271 = 0.9780 → 97.80 %
OEE = 0.8881 × 0.8611 × 0.9780 = 0.7479 → 74.79 %
Check it against the identity: 18 848 × 1.0 s = 18 848 s = 314.13 min of fully productive time, and 314.13 / 420 = 0.7479. Same answer.
The losses in minutes: 47 min to availability, 373 − 321.18 = 51.82 min to performance, and 423 × 1.0 s = 7.05 min to quality. Those three plus the 314.13 min of fully productive time sum back to the 420 min of planned production time, which is always true and is a good arithmetic check.
TEEP: utilisation is 420 / 1440 = 29.17%, so TEEP is 0.7479 × 0.2917 = 21.81%. A respectable 74.79% OEE on a single-shift pattern is 21.81% of the asset’s calendar capacity.
FAQ
Should changeover time count against availability? Yes, under the original six-big-losses framing — setup and adjustment is an availability loss. Excluding it because it is “planned” is the most common way OEE gets quietly inflated. If your business genuinely cannot reduce changeovers, track OEE with them included and report the changeover minutes separately, rather than deleting them from the denominator.
Can I average OEE across several machines? Not by taking the mean of the percentages — that weights a bottleneck the same as a rarely-used spare. Sum the fully productive time and sum the planned production time across the machines and divide. Better still, only track OEE on the constraint; improving a non-bottleneck’s OEE produces inventory, not output.
Why does rework count as a quality loss if the unit is eventually sold? Because the machine’s time was consumed making something that did not come off right first time. OEE measures first-pass yield, and the rework itself consumes further capacity somewhere. Count reworked units as defects.
What ideal cycle time should I use for a mixed-product shift? Weight it by volume: compute the ideal time for each product’s count, sum them, and divide by the total count to get an effective ideal cycle time — or, better, calculate OEE per product and combine the fully productive times. Using one product’s cycle time for a mixed shift is what usually produces a performance figure above 100%.
Is 100% OEE the goal? No. It is unreachable, and chasing it distorts behaviour — the fastest way to raise OEE is to reclassify downtime, not to reduce it. Use OEE to find which of the three factors is bleeding, fix that, and judge the result on output and cost.
Indicative figures for production analysis. Definitions of planned and unplanned time must be agreed and held constant within your own reporting standard before OEE trends mean anything.