By the end of this lesson
- Identify the capacity constraint in a simple serial process.
- Calculate throughput under capacity and demand limits.
- Explain why local utilization is not the same as system performance.
Follow the complete flow
An operation creates useful output through a sequence of activities. Increasing the speed of one activity is valuable only if the rest of the system can turn that capacity into the required output. Start with the entire flow, from incoming demand to a completed unit, before deciding where to invest.
Our teaching model has three serial stages: prepare, process, and inspect. Each unit must pass through every stage. Capacities are measured in units per hour, and customer demand is expressed in the same unit. The model assumes no rework, downtime, scrap, batching, or variability. Those assumptions make the arithmetic transparent but limit its application.
Find the minimum
If preparation can handle 80 units per hour, processing 50, and inspection 70, the maximum sustained output is 50 units per hour when demand is at least 50. Processing is the capacity bottleneck. Expanding inspection to 100 units per hour does not increase that maximum because processing still limits the flow.
Now suppose demand is only 40 units per hour. Output is limited by demand rather than the 50-unit processing stage. In this simplified system, throughput is the minimum of demand and the three stage capacities. The formula helps prevent confusing idle capacity with a problem that needs more investment.
Improve the constraint, then recalculate
Raise processing capacity from 50 to 75 in the simulator. Inspection, at 70, becomes the limiting stage. The identity of a bottleneck can change after an improvement. A credible capacity proposal therefore includes the next constraint, rather than extrapolating the first improvement indefinitely.
The utilization displayed here is throughput divided by each stage's capacity. It describes a simplified flow-balanced state; it is not an empirical measurement of worker effort. A non-bottleneck stage may have lower utilization while the process still delivers everything demand and the constraint permit.
Trying to run every stage at its maximum can create work-in-progress ahead of the constraint. The simulator does not model queues over time. Its purpose is to make the structural limit visible before adding more detailed assumptions about variability and waiting.
An applied capacity decision
Illustrative case: a manager wants a faster inspection machine because inspection looks busy during the afternoon. Before purchasing it, measure the complete process over an appropriate period. Check whether work arrives in batches, whether defects create repeat inspection, and whether the apparent pressure is persistent or temporary.
The investment could still be justified by quality, safety, flexibility, or resilience even if it does not increase maximum throughput. State that objective explicitly. A throughput model should not be used to dismiss benefits it does not measure, but those benefits also need their own evidence.
Use the simple calculation as a first diagnostic. Then collect observations on downtime, yield, setup times, and demand variation. Evaluate whether the same constraint persists across products and shifts. The appropriate next model depends on the question revealed by those observations.
Explore the model
Enable JavaScript to use this interactive model. The surrounding lesson explains its assumptions.
Keep the terms precise
- Capacity
- The maximum output rate of a stage under specified conditions.
- Throughput
- Completed units per unit of time.
- Bottleneck
- The stage that limits capacity in the defined process.
- Utilization
- Output rate divided by available capacity under the stated model.
Go deeper: assumptions and boundaries
A deterministic capacity model does not estimate waiting time. In variable systems, average capacity above average demand does not guarantee short queues. Product mix, rework loops, setup losses, and shared resources can require a more detailed process model.
A question worth thinking through
What objective might justify improving a non-bottleneck stage, and how would you measure that benefit?
Check your understanding
Check each answer and read the explanation. You can try again. Complete all 2 assessed questions to record the lesson as finished.
What to carry forward
- Measure all stages in compatible units.
- Recalculate the limiting stage after each change.
- Separate throughput benefits from quality, flexibility, and resilience benefits.
Sources & evidence
This lesson develops a transparent deterministic teaching model. Numerical results follow from the stated minimum-capacity rule; they are not external empirical findings.