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By Klaus-Peter Fähnrich, Thomas Meiren (auth.), Professor Dr.-Ing. Dieter Spath, Professor Dr.-Ing. habil. Dipl.-Math. Klaus-Peter Fähnrich (eds.)

The e-book files the cutting-edge in providers technological know-how. It combines contributions in carrier Engineering, provider administration and repair advertising and is helping to advance a roadmap for destiny R & D actions in those fields. The booklet is written for researchers in engineering and management.

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For the customers receiving service, their waiting time in queue is only a lower bound for their patience. There are statistical methods for ‘un-censoring’ data; see [10]. Another, more basic problem for estimating ș, is that most ACD data contain only averages, as opposed to call-by-call statistics that are required by the available ‘uncensoring’ methods. To this end, we suggest here two methods for estimating average patience. 1) between the probability to abandon and average wait. The average wait in queue, E[W], and the fraction of customers abandoning, P{Ab}, are in fact stan- The Palm/Erlang-A Queue, with Applications to Call Centers 29 dard ACD data outputs, thus, providing the means for estimating ș as follows: Tˆ P{Ab} E[W ] % Abandonment .

Furthermore, this calibration represents a form of validation of the model's assumptions, and can compensate for discrepancies. 6 Approximations Although exact formulae for the Erlang-A system are available and can be incorporated in software (see Sections 3 and 4) they are too complicated for providing guidelines and insights to call center researchers and managers. Consequently, some useful and insightful approximations have been developed, which we now describe. It has been found useful to distinguish three operational regimes, as in Garnett et al.

Here we demonstrate, as an example, two advanced capabilities of 4CallCenters. Example 1: Advanced profiling: One can vary any input parameters of the Erlang-A queue and display the corresponding model output (performance measures) either in a table or graphically. For example, let the average service time equal 2 minutes and average patience 3 minutes. Let the arrival rate vary from 40 to 230 calls per hour, in steps of 10, and the number of agents from 2 to 12. Then one can immediately produce a table that contains values of different performance measures for all combinations of the two input parameters.

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