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December 2009 Workload forecasting for a call center: Methodology and a case study
Sivan Aldor-Noiman, Paul D. Feigin, Avishai Mandelbaum
Ann. Appl. Stat. 3(4): 1403-1447 (December 2009). DOI: 10.1214/09-AOAS255


Today’s call center managers face multiple operational decision-making tasks. One of the most common is determining the weekly staffing levels to ensure customer satisfaction and meeting their needs while minimizing service costs. An initial step for producing the weekly schedule is forecasting the future system loads which involves predicting both arrival counts and average service times.

We introduce an arrival count model which is based on a mixed Poisson process approach. The model is applied to data from an Israeli Telecom company call center. In our model, we also consider the effect of events such as billing on the arrival process and we demonstrate how to incorporate them as exogenous variables in the model.

After obtaining the forecasted system load, in large call centers, a manager can choose to apply the QED (Quality-Efficiency Driven) regime’s “square-root staffing” rule in order to balance the offered-load per server with the quality of service. Implementing this staffing rule requires that the forecasted values of the arrival counts and average service times maintain certain levels of precision. We develop different goodness of fit criteria that help determine our model’s practical performance under the QED regime. These show that during most hours of the day the model can reach desired precision levels.


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Sivan Aldor-Noiman. Paul D. Feigin. Avishai Mandelbaum. "Workload forecasting for a call center: Methodology and a case study." Ann. Appl. Stat. 3 (4) 1403 - 1447, December 2009.


Published: December 2009
First available in Project Euclid: 1 March 2010

zbMATH: 1185.62204
MathSciNet: MR2752140
Digital Object Identifier: 10.1214/09-AOAS255

Keywords: call centers , exogenous variables , forecasting arrival count , QED regime , square-root staffing

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.3 • No. 4 • December 2009
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