Little's Law made visceral. Sliders drive a living queue — watch waiting time explode as utilisation nears 100%, price your batch size, and find the lever that actually clears your backlog.
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.
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.
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 a typical 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.
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.