Core lesson
Read the queue first
Set the conditions, then use the diagnosis to decide which trade-off deserves a closer look.
The queue, live
What the simulation says
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Optional lessons
Optional lessons from the same queue
Hold the queue conditions above constant, then isolate one trade-off at a time.
Optional lesson · release economics
The batch-size argument
How much should one release carry? The fixed cost of releasing says batch up; the cost of delay says ship now. The U-curve prices that trade with your numbers — demand comes from the slider above, so the cards share one world.
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Optional lesson · backlog recovery
Triage your queue
There's a pile today and everyone has a favourite fix. This ranks the four standard levers — more people, less intake, smaller items, a WIP limit — against your queue: the sliders above set the world, plus one number.
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Optional lesson · priority tax
Price the expedite lane
An expedite is not extra capacity. It jumps the same queue, so the saved waiting has to appear somewhere else. Set a small arrival rate and inspect the service-class trade.
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Optional lesson · dependency
Play the dependent dice
Five dependent steps each roll from the same distribution. Their local averages can look healthy while the system still accumulates waiting between them.
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Why waiting dominates
Little's Law says items-in-progress = arrival rate × time-in-system. Push a team toward 100% busy and the queue — not the work — sets your delivery date. Near full utilisation, small bumps in demand or item size multiply waiting non-linearly (the queueing curve Reinertsen built The Principles of Product Development Flow around). That's why the verdict splits the average item's calendar time into working and waiting — the waiting is usually the bigger number, and it's the one your process choices control.
The WIP limit slider shows the other half of the argument: in-progress items share the team, so WIP above capacity doesn't add throughput — it just stretches every item's calendar time. "Start less, finish more" falls out of the arithmetic. But note what a WIP limit can't do: if demand exceeds capacity, the backlog grows without bound and no limit fixes that — the tool says so, loudly, rather than hiding the queue upstream.
The batch-size card prices Reinertsen's other classic trade with the same demand number: a release's fixed transaction cost argues for batching up, the cost of delay argues for shipping now, and the U-shaped sum of the two has a floor — your economic batch size. Triage ranks the four standard backlog levers — more people, less intake, smaller items, a WIP limit — against the queue you actually have. The expedite lane makes the hidden tax of priority visible, while dependent dice shows why locally reasonable capacity cannot promise system flow.
One honest caveat: this is a flow model of one stage. Real teams aren't a single pipe, and constraints in human systems are often policy, mindset, or coordination rather than a visible logjam — a lesson the Theory of Constraints crowd learned the hard way. Use this to win the argument about overloading; use judgement for everything else.