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Cycle time vs lead time: which one to actually optimise for
Two metrics that sound interchangeable, point at opposite levers, and quietly decide whether your team works on the right thing.
Cycle time is what your team controls. Lead time is what your customer feels. Optimising the one without watching the other is how teams get faster at producing while their customers get unhappier.
The two metrics, plainly
Cycle time is the time a card spends actively in your production phases - the moment it enters phase 1 to the moment it leaves the last phase. Lead time is the time from the customer's order to the customer's delivery - it includes cycle time plus any waiting in the backlog before phase 1 plus any delay after phase N. The two are easy to conflate; on a calm day they're close. The day they diverge is the day you learn which one you've actually been optimising.
Why teams over-focus on cycle time
Cycle time is the metric your team owns end-to-end - it's measurable, it's improvable through internal effort, it shows up clearly on the production board. Lead time is partially out of the team's hands (when did the order arrive? how long did it sit in backlog before someone pulled it?). The temptation is to celebrate cycle-time improvements while lead time stagnates or grows. From the customer's seat, the team is not getting faster - their wait is the same or worse.
The case for watching lead time
Lead time is what the customer experiences. It's also the metric most production businesses lose contracts on - not on cycle time, which the customer never sees. A team that ships in 6 days of cycle time but waits another 12 days in backlog has an 18-day lead time; the customer remembers the 18, not the 6. Watching lead time exposes the part of the system you've been quietly ignoring (intake speed, backlog grooming, queue prioritisation).
The 30-day delta check
Once a month, look at the 30-day median cycle time and the 30-day median lead time. If both are roughly stable, the system is in balance. If cycle time fell while lead time stayed flat, the team got faster at production but the customer didn't notice - the queue ate the win. That's the signal that next month's work is in intake / backlog grooming, not in production tweaking.
When to optimise which
If lead time is much greater than cycle time, the constraint is queueing - work waits before it enters production. Action: cut the backlog (prioritise older items), tighten intake quality (don't accept work the team can't pick up), or raise WIP at the bottleneck phase if it's truly under-staffed. If cycle time and lead time are close, the queue isn't your problem; the production phases are. Action: bottleneck analysis (see /topics/finding-your-bottleneck) within the production phases. Production Board surfaces both metrics on the dashboard so you can see at a glance which kind of problem you have today.
The composite metric most teams should actually use
If you only ever look at one number, look at delivery-on-promise rate: percent of cards delivered by the date you promised the customer. It folds both cycle time + lead time into a single signal that aligns with what the customer cares about. A team optimising delivery-on-promise rate naturally watches both metrics, because both feed into it. A team chasing pure cycle time often improves the wrong thing.
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