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Availability and utilisation: the time the line is actually up<\/h2>\n
Availability is one of OEE’s three building blocks: out of the required production time, what share is the machine actually up and running rather than stopped? On its own it’s a useful diagnostic – it isolates whether your losses come from downtime, from speed, or from quality – but it tells only part of the story.<\/p>\n
Looked at more broadly, utilisation asks how much of the available time is genuinely turned into running time. It’s a helpful organisational lens on the workshop, but it shouldn’t be confused with OEE: a high availability can still hide heavy speed and quality losses that only the full OEE calculation will reveal.<\/p>\n
TEEP: how much of your installed capacity you really exploit<\/h2>\n
TEEP, Total Effective Equipment Performance, widens the frame to the total calendar time – 24 hours a day, 7 days a week. It’s the broadest view of all, including even the periods when the plant is closed: nights, weekends, unscheduled shifts.<\/p>\n
It’s used mainly for management-level steering, to reason in terms of installed capacity and investment decisions: how much of my theoretical capacity am I actually exploiting? TEEP is therefore always lower than or equal to OEE over the same period, because its denominator is larger.<\/p>\n
A worked example to fix the ideas<\/h2>\n
Take a single line. Over its required production time it posts an OEE of 75%. But the workshop has breaks, planned maintenance and periods with no load: measured over the open hours, utilisation drops to, say, 55%. And if you relate output to total calendar time – nights and weekends included – TEEP falls lower still.<\/p>\n
These three numbers don’t contradict each other. They describe the same line at three different time scales. The problem only appears when you compare them without realising it, or take one for the other in a report.<\/p>\n
The most common mistake: comparing different time bases<\/h2>\n
The classic trap is to line up an OEE figure against a TEEP figure as though they were comparable. If one team steers by OEE and another reports on a broader time base without saying so, the trade-offs are made on incompatible foundations and the targets become unreadable.<\/p>\n
So the first discipline is to state the time base explicitly before any comparison. An OEE target and a TEEP target are not discussed the same way, and a reported gain only means something when it’s tied to a constant time base.<\/p>\n
The hidden trap: the quality of the measurement<\/h2>\n
The second trap is subtler. Whichever metric you choose, its quality depends entirely on the data feeding it. An OEE calculated from manual readings inherits all their gaps: unseen micro-stops, ignored speed losses.<\/p>\n
Switching metric corrects nothing if the underlying measurement stays approximate. A TEEP built on a poor measurement is no more reliable than an OEE built on the same poor measurement. The real question, then, is less the choice of acronym than the reliability of the measurement underneath it.<\/p>\n
So which one should you steer by day to day?<\/h2>\n
To steer at the level of the line and the workshop, OEE is the working metric: it’s the one that tells you where to act, day after day. TEEP and broader utilisation keep all their value for capacity reading and investment decisions – but they’re built from the same shop-floor data.<\/p>\n
Real-time measurement at the source, like TeepTrak’s, reads availability, rate and quality directly on the machine, continuously, with no MES project and installation in under an hour. On that common base, OEE, utilisation and TEEP become consistent with one another. Hutchinson improved its OEE from 42% to 75% with the same headcount and machines, sensor installed in under an hour.<\/p>\n
OEE: the starting point of any improvement<\/h2>\n
If a single metric had to guide a line’s daily life, it would be OEE. It focuses attention on the time that’s genuinely useful and translates availability, rate and quality into one number. It’s on OEE that improvement projects, team targets and line-to-line comparisons are built.<\/p>\n
TEEP and utilisation come as a complement, for decisions of a different order: workshop organisation, sizing, investment. Confusing them amounts to mixing operational steering with capacity strategy – two timeframes that don’t call for the same actions or the same people.<\/p>\n
Why the metrics complement rather than compete<\/h2>\n
A mature site doesn’t choose between OEE, utilisation and TEEP: it uses them at different levels. The shift leader steers the line on OEE, the production manager reads the workshop on its open hours, and management reasons in installed capacity on TEEP. Each looks at the perimeter that concerns them.<\/p>\n
The condition for this nesting to work is that all three rest on the same base data. That’s exactly what a single, automatic, continuous measurement provides: the numbers stop contradicting each other from one level to the next, and trade-offs become readable at every floor of the organisation.<\/p>\n
A bad comparison to avoid<\/h2>\n
Picture two workshops in the same group. The first proudly announces a ‘yield’ of 78%, the second a ‘yield’ of 56%. Management concludes that the second is in trouble and considers action. In reality, the first was reporting OEE (required time) and the second a broader, open-time figure: the two sites might have been at exactly the same real level.<\/p>\n
This kind of mix-up is far more common than people think, and it distorts trade-offs, bonuses and investment decisions. The cure is simple but demanding: standardise the time base and the measurement method across the whole group, so that every number means exactly the same thing everywhere, and a site-to-site comparison is finally legitimate.<\/p>\n
Key takeaways<\/h2>\n
OEE to steer the line (required time), utilisation to read the workshop (open time), TEEP to reason in capacity (total time). Never compare two metrics on different time bases without saying so. And above all: the reliability of all three depends on one thing – the quality of the measurement.<\/p>\n
Real-time measurement at the source makes them consistent and usable, from the workstation right up to the management committee. More than 450 plants across 30+ countries already monitor their OEE to the second with TeepTrak.<\/p>\n
FAQ<\/h2>\n
What’s the difference between OEE and TEEP?<\/strong>
OEE is calculated over the required production time (when the machine is supposed to run). TEEP is calculated over the total calendar time, 24\/7, including closed periods. TEEP is therefore lower than or equal to OEE over the same period.<\/p>\nAre OEE and TRS the same thing?<\/strong>
Yes. TRS is the French term, OEE (Overall Equipment Effectiveness) the English one. Same definition, same formula: Availability x Performance x Quality.<\/p>\nWhat is TEEP?<\/strong>
Total Effective Equipment Performance: output related to total calendar time (24\/7), used to reason in terms of installed capacity and investment decisions.<\/p>\nWhich metric should I track to steer a line?<\/strong>
OEE, because it focuses on the time the machine is supposed to be producing and points directly to where to act day to day.<\/p>\nWhy aren’t my OEE, utilisation and TEEP consistent with each other?<\/strong>
Usually because they don’t rest on the same base data. A single, automatic, continuous measurement makes all three consistent.<\/p>\n\n
Request a demo, your real OEE in days<\/div>\n
Sensor installed in under an hour, no MES project, no production stop. First results within the first week.<\/div>\n
Request a demo \u2192<\/a><\/div>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"
[et_pb_section fb_built=”1″ _builder_version=”4.27″][et_pb_row][et_pb_column type=”4_4″][et_pb_text] OEE, TEEP, availability: which metric should you actually track? Key takeaways OEE, TEEP and availability measure effectiveness on different time bases – they answer different questions. OEE is the day-to-day driving metric; TEEP frames installed-capacity and investment decisions. Never compare two metrics built on different time bases without saying so – […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","ai_seo_title":"","ai_meta_description":"","ai_focus_keyword":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-118645","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\n
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![\"OEE,](\"https:\/\/www.teepchina.com\/wp-content\/uploads\/2026\/06\/oee-teep-which-metric-track.png\")
<\/p>\n