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What is the difference between cycle time and lead time?

Lead time measures from when a request is made to when it is delivered — the customer's whole wait. Cycle time measures from when work actually starts to when it is done — the team's execution. Lead time minus cycle time is queue time, and in most teams the queue is the larger number.

The number that embarrasses teams

Teams optimise cycle time because it is the part they control, and then wonder why customers still complain. If a request waits three weeks in the backlog and takes two days to build, the customer experienced twenty-three days. Measuring only cycle time makes the biggest chunk of the wait invisible.

How to reduce each

Cycle time falls with smaller items, WIP limits and faster review. Lead time falls with fewer things in the queue at all — saying no earlier, deleting stale backlog items, and being honest that a hundred-item backlog is a hundred promises you are not keeping.

Measuring them on a board

Cycle time is the interval from entering In Progress to reaching Done; lead time runs from creation to Done. Flocci Projects records created and updated timestamps on every issue plus a full activity history of status changes, so both are derivable — though there is no built-in cycle-time report today.

Try it on a free Flocci Projects board

Related questions

Which should a small team track?

Lead time, if you only track one, because it is what the person who asked actually experiences. Add cycle time when you want to know whether the problem is your execution or your queue.

How do these relate to velocity?

Velocity measures throughput per sprint; cycle and lead time measure duration per item. A team can have healthy velocity and terrible lead time if the backlog is a landfill — the metrics answer different questions and neither substitutes for the other.

What is a good cycle time?

There is no universal number, but a useful target is that most items finish within a few days. If typical cycle time approaches your whole sprint length, your items are too large to forecast reliably.

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