Little's Law is a fundamental rule of queueing theory from 1961 (John D. C. Little, MIT) that describes the relationship between work in progress (WIP), throughput, and cycle time/lead time. The formal equation is:
WIP = Throughput × Cycle Time
or, in classical queueing theory: L = λ × W, where L is the number in the system and λ is the arrival or departure rate. This law applies regardless of distribution assumptions or system details—provided that the conditions are stationary.
Practical relevance
Little's Law is groundbreaking in lean, Kanban, and agile methods:
• WIP (Work in Progress): Number of current tasks in progress.
• Throughput: Rate at which work is completed (e.g., items/day).
• Cycle time: Average time required to complete an item.
Application example: Reducing WIP items directly shortens cycle time while maintaining throughput. The lever is clear: either reduce WIP to deliver faster, or increase throughput, which is often more technically or personnel-intensive.
Typical misunderstandings
• "More WIP is more efficient" – on the contrary: higher WIP increases lead times and reduces predictability.
• "Little's Law only applies to production" – it is universally applicable to services, software, Kanban systems, call centers, etc.
• "You can reduce cycle time without affecting WIP or throughput" – only possible to a limited extent; usually requires capacity adjustment or backlog control.
Relevance for organizations
• Predictability: If two variables are known, the third can be reliably derived.
• Flow control: Low WIP – shorter throughput time – higher quality.
• Tool for measuring impact: Small changes (e.g., WIP limits) have measurable effects on cycle time.
• Investment decisions: Helpful, for example, when weighing capacity expansion vs. process optimization.
Practical example
A team visualized their work items on a Kanban board: With 32 items in progress (WIP) and a throughput of 1.25 items per day, the cycle time was approximately 25.6 days. Reducing the WIP to 24 items resulted in a significantly faster delivery time – practical proof of the law.
Extensions and applications
• Queue chains/networks: Little's Law also applies to chained systems, e.g., multi-team workflows or portfolios.
• Transient Cases: Little's Law can also provide important insights in change scenarios (e.g., during the onboarding of measures).
• Simulation & scenario models: In combination with tools such as cumulative flow diagrams and probability distributions, Little's Law can be used for forecasting and capacity planning.
How good coaches work with Little's Law
• Create a database: Measure WIP, throughput, and cycle time in uniform time windows.
• Visualize scenarios: What happens when WIP is reduced or throughput is increased?
• Set & check WIP limits: Teams experiment with real values (e.g., max. 3–5 items at a time).
• Identify bottlenecks: Where are the bottlenecks? (Use cumulative flow diagram + Little's Law)
• Use in the portfolio: Derive queue overload and necessary control of large initiatives.
CALADE perspective
We routinely use Little's Law as an indicative flow lever, not as mathematical dogma. In projects or programs, we instrument teams, ARTs, and portfolios in such a way that WIP, throughput, and lead time become measurable—data instead of gut feeling. In combination with WIP limits, Kanban, and portfolio control, we create iteratively measurable flow improvement. Our coaches understand these principles and actively apply them – as a structuring thought model, not as an empty formula.
Related terms
• Kanban / WIP limits
• Lead time vs. throughput time
• Cumulative flow diagram (CFD)
• Queueing Theory
• Throughput Accounting
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