How would you apply Little's Law to optimize Kanban WIP limits?
Source: Wikipedia: Little's lawadvanced
Tests whether you can operationalize queueing theory in Kanban. A strong answer cites L equals lambda times W, fixes throughput, then solves for a WIP limit that yields a target cycle time.
Tests whether you can move beyond Agile platitudes and use queueing theory to set data-informed WIP limits. A strong answer defines L as average WIP, lambda as throughput, and W as cycle time; assumes stability; then algebraically isolates the variable you want to control. For example, holding throughput constant, halving WIP should halve cycle time, so you argue for a new limit using historical arrival rates.
Read the original → Wikipedia: Little's law
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- #little's law
- #wip limits
- #queueing theory
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