Little's Law

Little's law states that the average number of items in a stable system (L) equals their average arrival rate (λ) times their average time in the system (W). If 20 deals enter your pipeline weekly and a deal averages 6 weeks inside, the pipeline holds 120 deals — necessarily. The theorem's power is its generality: it holds for any stable queue regardless of arrival patterns or processing order — supermarket lines, support tickets, sales pipelines, content backlogs.

Rearranged, it answers operating questions: W = L/λ turns a pipeline count and a close rate into TRUE average cycle time (often embarrassingly longer than the CRM's claimed one); λ = L/W converts work-in-progress and cycle time into real throughput. The law's sharpest managerial edge: pushing more INTO a system without raising processing capacity doesn't increase output — it increases L and therefore W. The bloated pipeline isn't growth; it's the same throughput waiting longer.

It matters wherever flow masquerades as stock: pipeline reviews that celebrate size (the law converts size into waiting time on the spot), content operations (a 60-item backlog at 5 published/week = 12 weeks of staleness by theorem), and capacity planning for SDR teams and support queues. It is also the quiet logic of WIP limits in agile and lean — capping L to cut W. For marketers it's the rare piece of operations math that fits on a sticky note and wins arguments.

Named for MIT operations researcher John D.C. Little, who published the first general proof of L = λW in Operations Research in 1961 (the relationship had circulated as folklore); Little — also a marketing-science pioneer — lived to see his queueing identity run sales pipelines he never anticipated.

Etymology: source.

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