Throughput vs Capacity
Why Rated Speed Never Shows Up

The line is rated at 60 a minute. The shift report says it averaged well under that, and every machine on it can run at 60. Capacity is what the line could make. Throughput is what it does make. The difference isn’t a rounding error: it is the line’s losses, and most of them sit where a capacity sheet never looks.

Throughput vs capacity: the definitions

The two words get used interchangeably, and most confusion about line performance starts there. Three quantities are worth keeping apart:

Throughput is always the smallest of the three. Capacity is a property of the equipment and the plan. Throughput is a property of the line as it runs, including every unplanned stop, every slow stretch, every rejected unit and every minute a healthy machine spent waiting on another one.

The throughput formula

For a single machine the throughput formula follows directly from OEE:

For a line, the same formula holds with the line’s rated speed and line OEE, which is not any machine’s OEE and not their average (see line OEE vs machine OEE). That is where the formula stops being useful as a prediction. You can measure line OEE after the fact. You can’t compute it in advance from the machines alone, because it depends on how their losses interact.

Illustrative arithmetic, not plant data

Take a machine rated at 60 units per minute that goes down for a 90-second jam every 12 minutes. Out of every 13.5 minutes it runs for 12, so it is available 88.9% of the time and its effective rate is about 53.3 per minute before any other loss. Over a 480-minute shift that is 25,600 units against a rated 28,800. The rated figure never shows up on the report, because the machine never runs for 480 uninterrupted minutes.

Where capacity goes before it becomes throughput

Between rated capacity and throughput, capacity leaks away in five places. The first three are the familiar OEE losses. The last two don’t appear on any single machine’s report.

1. Changeovers and planned stops

Every format change, sanitation cycle and planned stop takes the line out of flow. These losses take you from rated to effective capacity, and they are usually the best-documented. They are not always the ones that matter. At Bell-Carter Foods the two-hour changeovers on the pitting machines looked decisive; a capacity model found the binding constraints downstream in packaging (case study).

2. Breakdowns

Unplanned downtime is the loss everyone sees. What it costs in throughput depends on where it happens: a stop on a machine feeding a full buffer can cost almost nothing, while the same stop on the constraint costs its full length in finished product.

3. Micro-stops and speed loss

Short stops and running below rated speed land in Performance, and a report often records them as a slower rate rather than as stops. They are frequent enough to keep buffers from refilling, which lets them cost more throughput than their minutes suggest. Micro-stops vs breakdowns covers why.

4. Starving and blocking

A machine that is starved has capacity and nothing to work on. A machine that is blocked has capacity and nowhere to put its output. Either way, that capacity is real, paid for, and unusable. It never shows as downtime on the machine that loses it, which is why a line can report healthy machines and poor output at once. Starved vs blocked explains how to read which is which.

5. Quality losses

A rejected unit consumed capacity at every machine it passed through. Scrap near the end of the line costs more capacity than scrap near the start.

The constraint sets throughput

A series line can deliver no more than its most limiting resource allows at that moment. That is the premise of the theory of constraints, and it has a blunt consequence for capacity: adding capacity anywhere except the constraint adds none to throughput. A faster machine upstream of the constraint fills the buffer and then blocks. A faster machine downstream starves.

The practical difficulty is finding the constraint, because it isn’t fixed. When a machine stops and the storage beside it runs out, that machine limits the line, whatever its rated speed. Minutes later something else does. Product mix and the schedule move it as well.

The signals that locate it are the ones in the hidden bottleneck: the constraint is the machine that is neither starved nor blocked, buffers that run full point downstream to it, and buffers that run empty point upstream. A machine that is mostly blocked is telling you its extra capacity is already unused.

Why rated capacity never shows up

A capacity spreadsheet takes each machine’s rate, applies an average availability and takes the minimum. The answer is always too high, and not by chance. Averaging removes the variability that makes a real line fall short: the stop that outlasts a buffer, the run of stops that drains it, the stoppage that cascades into three other machines. Spreadsheet, AI, or simulation? explains why the error only runs one way.

On ReliaSim’s Buffer-Options demo bottling line, the same five machines and three buffers make 525,600 pallets in a year with every failure mode removed. That is the line’s capacity. With its real failure modes the line makes 286,423 pallets and runs at 54.5% efficiency (The Hidden Factory). Nothing about the rated speeds changed between the two runs. The difference is losses, and how they interact across the line.

Planning capacity against throughput

The useful questions are about throughput, not capacity: will this line make next year’s volume, and if not, which change closes the gap? Answering them means modeling the line as it runs: each machine’s rate, each failure mode’s time-to-failure and time-to-repair distributions, the buffers and conveyors between them, and the schedule. Validate that model against the line’s own history, then test each option: a faster machine, a bigger buffer, a removed failure mode, an extra shift.

Built from per-failure-mode interrupt data and validated against line history, a ReliaSim model can match measured OEE within 1%. The published Fischel & Lange (WSC 2020) food-plant model, rebuilt in ReliaSim and independently validated by Tom Lange, is the public proof point (Within 1% of measured OEE). With a validated model, capacity planning becomes a set of runs instead of a set of assumptions. See capacity planning simulation for the method.

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