Seventy Per Cent Capacity Is Not Thirty Per Cent Waste
Published 9/23/2026 · 3 min read · Business tools
Lena Hoffmann — Science & education writer at OneKitly
Mathematics · Physics
Checked against 2 sources
Capacity utilisation is actual output divided by the maximum the plant could sustain: 7,000 against 10,000 is 70 %. The instinct that follows — that the missing 30 % is money left on the floor — is where the reasoning usually goes wrong, because a system pushed towards 100 % does not simply produce more. Queueing theory gives waiting time a term of 1 ÷ (1 − utilisation), which is 3.3 at 70 % and 20 at 95 %: a six-fold rise in queueing for a quarter more throughput. That reserve absorbs the machine that breaks, the order that arrives late and the shift that runs short, and a plant that has spent it has nothing left to absorb anything with. The right question is not how to raise 70 %, but whether the delivery promises the business makes are compatible with the reserve it has chosen to keep.
7,000 units made against a capacity of 10,000 is 70 % utilisation. The reflex is to call the other 30 % a loss; queueing theory says it is the reason the line still delivers on time.
Which capacity is in the denominator
The same plant has at least three capacities: what the machines could do running flat out, what the current shift pattern allows, and what the tightest step in the chain permits. Choosing the first gives a flattering-looking problem — a big gap to close — while the third gives the only number that can be acted on, because a line runs at the speed of its slowest station and every other station's spare capacity is decoration. State which capacity you divided by, or the percentage is not comparable with anyone else's, including your own from last quarter.
Utilisation is not effectiveness
A line can run at 95 % utilisation and produce scrap, or run fast on a product nobody ordered. Utilisation counts hours filled; it says nothing about whether the output was good, needed, or worth making. That is why plants that manage this figure alone tend to accumulate finished goods — the cheapest way to raise utilisation is to keep making something, and inventory is where the something goes. Pair it with a yield or quality measure before letting anyone set a target on it.
| Utilisation | Output | Queueing factor |
|---|---|---|
| 70 % | 7,000 | 3.3 |
| 85 % | 8,500 | 6.7 |
| 95 % | 9,500 | 20 |
Worked with our own calculator
Capacity utilization calculator
Given
- Actual output
- 800
- Maximum capacity
- 1,000
Result
- Capacity utilization
- 80%
These figures are produced by the calculator below, not typed in by hand — they are recomputed whenever the tool changes.
Run it on your own figures →Frequently asked questions
- So what utilisation should we aim for?
- It depends on how variable the work is, and that is the whole answer. A process with predictable arrivals and identical jobs can sit near 90 % without queues building; a workshop with urgent one-off jobs and unreliable inputs starts choking well before 80 %. Rather than borrow a target, plot your own lead times against your own utilisation over a few months — the point where the curve turns upwards is your ceiling, and it is a fact about your variability, not about the industry.
- Does this apply outside manufacturing?
- Yes, and the effect is sharper where the capacity is people. Booking a team at 100 % of its hours leaves nothing for the interruption, the sick day or the piece of work that turns out to be twice its estimate, so the plan fails on contact rather than absorbing. The same 1 ÷ (1 − u) shape governs a hospital's beds, a consultancy's billable weeks and a support queue — which is why the well-run ones deliberately plan below full.
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