Alexey Shurov.
Free tool

Flow efficiency and cycle time calculator

Two numbers that show where time really goes. Flow efficiency shows how much of an item's life is waiting, and Little's Law turns work in progress into the time a new item takes to get through.

How much of your cycle time is waiting

Time someone is actually working on it.
Calendar hours from when it arrives to when it is done.

How long work takes to get through, from what is in progress

Started but not finished, across the team.
A typical recent week.

Flow efficiency

Flow efficiency is the hands-on time on an item divided by the total time from start to finish. If a task takes three hours of work but three days to finish, most of its life is spent waiting in a queue, for a reply, an approval or a free person.

The useful part is not the percentage itself but where the waiting happens. Split the time from start to finish into stages and measure the waiting in each, and the stage with the longest wait is the bottleneck.

Little's Law

Little's Law says the average time an item takes to get through a process equals the work in progress divided by the rate items are finished. It holds for any stable process, a support queue, an order desk or a pull request queue.

It explains why starting less often finishes things sooner. With the same team and the same speed, halving the work in progress halves the time a new item waits.

Common questions

What is flow efficiency?

Flow efficiency is the share of an item's total time from start to finish that someone is actively working on it. The rest is waiting. It is a quick way to show that a slow process is usually a waiting problem rather than a working-speed problem.

How do you calculate flow efficiency?

Divide the hands-on time on an item by the total time from when it arrived to when it was done, and multiply by 100. For example three hours of work over a 72-hour cycle is about 4 percent flow efficiency.

What is Little's Law?

Little's Law states that average time through a process equals work in progress divided by throughput. With 60 items in progress and 20 finished a week, a new item takes about three weeks on average to get through.

How do I find the bottleneck from these numbers?

Break the time from start to finish into stages and record when each item enters and leaves each one. The stage where items wait longest is the constraint. Fix that one first, then measure again, because the bottleneck moves once it is cleared.

Want the real numbers for your process

I build and run production AI agents that take repetitive work off operational teams. Send me the task and I will tell you honestly whether it is worth automating.

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