Three numbers describe the same machine and disagree by thirty points, because they divide by different denominators. This explains what each one measures, which decision each is for, and why 85% is a worse target than the number you started from.
OEE is availability x performance x quality measured across scheduled production time — of the hours you planned to produce, how many produced good parts at rate. TEEP is OEE multiplied by utilisation, measured against all calendar time, so it also counts the hours you never scheduled. A machine running one shift a week can post 85% OEE and 20% TEEP at the same time: the first says the shift went well, the second says most of the asset is idle.
OEE is availability × performance × quality, measured across scheduled production time. It answers: of the time we planned to produce, how much produced good parts at rate? It came out of Total Productive Maintenance, developed by Seiichi Nakajima at the Japan Institute of Plant Maintenance.
Utilisation is scheduled time divided by calendar time. It is a management decision, not a machine property — a plant running one shift has low utilisation and can have excellent OEE.
TEEP is OEE × utilisation, measured against all 8,760 hours in a year. It answers a different question: how much of the capacity you own are you actually using?
Take a machine scheduled for one eight-hour shift, five days a week. That is 40 of the 168 hours in a week, so utilisation is 23.8%.
Say it runs well during those 40 hours: 90% availability, 95% performance, 99% quality. OEE is 0.90 × 0.95 × 0.99 = 84.6%.
TEEP is 84.6% × 23.8% = 20.1%. The machine is excellent and the asset is idle. Both statements are true, and only TEEP shows the second one.
This is why arguing about which number is right wastes time. They answer different questions. A plant chasing OEE will tune changeovers; a plant chasing TEEP will add a shift. Those are different budgets and different decisions.
85% is the figure everyone quotes, and it comes from multiplying Nakajima\u2019s component targets: 90% availability, 95% performance, 99% quality. OEE.com puts it plainly — "it is often thought that a World-Class OEE score is 85%" — while warning against fixating on the absolute value.
It is also a benchmark from 1970s Japanese manufacturing, and it does not transfer evenly. Published sector ranges differ substantially: roughly 60–70% for aerospace, 65–75% for automotive, 55–65% for gear manufacturing. A job shop with high product mix is not comparing like with like.
The more useful framing, in Leanworx\u2019s words: "going from 50% to 60% OEE is more valuable than chasing a global average that may not apply to your shop floor."
Lower than most plants expect before they measure. OEE.com reports that "most manufacturing companies, even today, have OEE scores closer to 60%", and that it sees "more companies with OEE scores lower than 45% than companies with OEE scores higher than 85%".
Leanworx puts the typical range at 55–65%, and notes that plants measuring for the first time often find themselves at 40–55%.
Expect the first measured number to be worse than the number currently believed. That gap is the finding, not a failure of the measurement.
OEE, per machine, with reason-coded downtime beneath it. It is the only one of the three a shift can act on, and without the reason codes it is a score rather than a diagnosis.
Report TEEP quarterly, to the people who approve capital. It is the number that answers whether a second machine is needed or a second shift would do — and it frequently shows that the constraint is scheduling rather than capacity.
Utilisation on its own is rarely worth displaying. It measures a decision that has already been made, and low utilisation is often correct.
OEE is not defined by one universal standard, which is part of why vendors report it differently. The original framework is Nakajima\u2019s, through the JIPM. National and sector standards exist alongside it — AFNOR NF E60-182 in France, and SEMI E10 for semiconductor equipment, which defines its own state model.
The practical consequence: before comparing your OEE against anyone else\u2019s, check whether planned downtime, changeover and no-demand time sit inside or outside their denominator. Two plants can differ by twenty points on definition alone.
Coneqt OEE reports availability, performance, quality and OEE per machine, line and site, with reason-coded downtime that separates setup and changeover from breakdown. Utilisation follows from the shift calendar, and TEEP from the two together.
The measurement is taken from the machine\u2019s existing control through a gateway, so the numbers are read rather than typed. Pricing is ₹499–1,499 per machine per month; hardware, where a site needs it, is quoted separately.
Every price and range on this page comes from one of these, checked September 2026.
Send us the machine list. You get subscription plus any gateway cost in one figure, in writing, before anything is installed.
A two-week pilot on a single line, on your data, with your team. No rip-and-replace and no new hardware in most plants.
We use only what this site needs to work, plus aggregate traffic counts. No advertising trackers, and we do not sell audience data.