other queuing and staffing models
As well as the Erlang C model there are several
other call center staffing models in use.
Simulation
This is the most accurate way of modeling as it
simulates thousands of calls arriving using a 'Monte Carlo' simulation. It can
also handle a mix of call types and agent skills.
Unfortunately no low-cost call center calculator
uses simulation at present. The other disadvantage of simulation is that it is very compute
intensive and can be quite slow.
'Merlang'
Merlang (or modified erlang) is an attempt by one
vendor to overcome two of the restrictive assumptions of the Erlang C model. It
adds another two parameters to the calculations - 'Mean time to abandon' (or MTA)
and 'Expected Retry Percent'.
However there are several obvious issues with this
approach -
| Parameter |
Notes |
| Mean
Time to Abandon |
This is not
available from any ACD statistics and must simply be guessed.
There
is actually no such thing as a 'mean' time to abandon - some callers hang
up immediately on reaching the queue; others will hang on virtually
forever. The relationship between abandonment rate and time on hold is
quite complex and varies very much by the type of caller, the nature of
their problem and their expectations.
The result is that
any estimated MTA probably has an accuracy of no better than 20%.
(Actually improving the 'Wrapup Time' parameter has a much greater impact
than attempting to include a MTA). |
| Expected
Retry Percent |
This is the
percentage of callers who, after abandoning, will call again some time
later.
It
can only be guessed at.
In addition, retries
will generally arrive in a later 'interval' - this is not handled by
Merlang. |
This model
also does not address another Erlang assumption of a 'steady state' situation
where all calls are handled in the same interval in which they arrive (i.e.
there is never any backlog of calls impacting subsequent time periods).
Our conclusion is that in trying to solve one
problem, Merlang actually introduces several others.
| 
