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Workforce
Simulation
This tool is useful to make Staffing, that is to know how many agents are needed in my Contact Center to answer calls, also it is interesting to make ForeCasting estimating the volume of growth of the Contact Center to know how many agents are going to be needed to reach those desired metrics of Service Level and Abandon.
Erlang
The Erlang distribution is a two parameter family of continuous probability distributions 
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Are good for Staffing, know how many agents I need in my Contact Center also is great for ForeCasting using the metrics of Service Level and abandon rate.
Erlang C
Consider the classic M/M/c queueing model: Poisson arrival process, exponential service time and no abandonment by the customers (that is, customers have infinite patience time). The queue capacity can be finite or infinite. Because there are no abandonment's, the number of servers must be greater than the offered load for the queue to be stable and reach steady state. Let λ be the arrival rate and 1/μ the average waiting time, then the offered load is ρ=λ/μ.
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The chart shows the mean of wait for the clients including abandon and answered for those Agents.
Erlang A
Consider the following M/M/c+M queueing model: Poisson arrival process, exponential service time and exponential patience time. This queueing system is always stable because of customer abandonment's. Also due to abandonment's, there are different ways to account the customers that abandoned in the service level (SL) function.(SL1 and SL2) for the SL and one approximate formula (SLD) based on diffusion equations. The choice of an exact or approximate SL formula will also decide on whether this program will use the exact or approximate formulas for the other performance measures (abandonment ratio, delay probability and average waiting time).
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The chart shows the mean of wait for the clients including abandon and answered for those Agents.
Erlang B
We assume the classic M/M/c queueing model with Poisson arrival process, exponential service time and no queueing capacity. That is, any new customer that sees all agents busy at their time of arrival will be blocked from the system and forced to abandon. The number of states in the system is equal to: number of servers + 1 (empty state). With a minimum of 1 agent, this queueing system is always stable, because there is no waiting queue. All non-blocked customers are automatically served and their waiting time is 0.
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