Two powerful tools in one: Little's Law for flow analysis, and M/M/c for multi-server queue optimization.
Little's Law states that the average number of items in a stable system equals the average arrival rate multiplied by the average time each item spends in the system. It applies universally — from factory floors to software Kanban boards to service queues.
Enter any 2 values and the third will be calculated automatically.
The M/M/c model extends single-server queueing to multiple parallel servers (attendants, machines, agents). It answers the critical question: "How many servers do I need to keep wait times acceptable?" Uses the Erlang C formula to calculate the probability of waiting, expected queue length, and average wait time.
Enter all 3 inputs to get full queue performance metrics.
Little's Law and queueing theory are just the beginning. JJ Andrade helps businesses reduce cycle times, right-size their teams, and increase throughput using data-driven methodology.
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