# Erlang distribution

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## Erlang distribution

[′er‚läŋ ‚dis·trə‚byü·shən]
(statistics)
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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They also use Erlang C for staffing levels, which was invented by A.K.
Erlang C assumes that callers will remain in queue-which may had been true in 1917 with telephone users at the time willing to wait for a long time before being connected, but definitely not today, especially in the "want it now" environment.
4.3 Representation of the Erlang C formula through Erlang B formula
By these modifications we can state the Erlang C formula in the form [4]:
To manage the ACD queue in the most efficient way possible, the Erlang C algorithm is used to determine optimum staffing requirements.
Moreover, these 67 staff would "be occupied and answering questions 89 percent of the time." However, if the Erlang C algorithm was told that 116 staff would be working to answer all the questions, the staff occupancy rate would be only 51%, "which means they are spending half their time doing something other than reference" (p.
To model the random arrival of incoming calls, the resources required to handle them, and the resulting service associated with that staff level, a special technique called Erlang C modeling is widely used in the call center industry.
We utilize an Erlang C calculation with 20 hours of traffic (workload) and determine the number of agents that meets our desired service goal.
Most call centers use a model called Erlang C that takes into account the randomness of the arriving workload as well as the queuing behavior (holding for the first available agent) of the calls.
Let's take a look at Erlang C predictions based on the 20 hours of workload we defined earlier.
Whether you've done your forecasts and schedules with workforce management software, spreadsheets or the old-fashioned way with a pencil and a calculator, you've likely arrived at your numbers and predictions of service with an Erlang C model.
The Erlang C model takes this randomness of call arrivals into account when determining staff requirements and predicting service levels.

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