# Little's Law More work in progress means longer wait times. Cycle time equals WIP divided by throughput. If you want things to move faster, adding more work makes it worse. The only levers are finishing more or starting less. --- ## The formula **L = λW** - the average number of items in a system equals the throughput rate times the average time each item spends there. Rearranged: **W = L/λ**. Wait time is WIP divided by throughput. This holds under remarkably general conditions. It doesn't matter how work arrives, how it's prioritised, or how variable the processing times are. As long as the system is stable, Little's Law applies. If your throughput is fixed - and in most organisations it effectively is - then every extra item in the queue lengthens the wait for everything else. Start a new initiative before finishing the last three, and all four take longer. This is why "just start it" is so destructive. Each new project feels costless, but it taxes every other project in the system. --- [[Execution trap]] builds its entire argument on Little's Law. The essay shows a team with forty-seven items in flight and throughput of six per month: the formula predicts eight-month lead times before you look at a single person's performance. When leaders diagnose an execution problem and respond by adding more work - more initiatives, more oversight, more reporting - Little's Law says this is exactly backwards. More WIP at constant throughput means longer cycle times. The "fix" amplifies the problem. Finishing before starting is Little's Law translated into a management principle. If throughput is roughly fixed, the only fast lever for reducing lead time is reducing WIP. Every time you say yes to a new project, you're not just committing resources - you're lengthening the lead time on everything else in the queue.