CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

11. Klungle, Roger and Jim Maluchnik. Call center forecasting at AAA Michigan, The Journal of
Business Forecasting Methods & Systems, 16 (4), 1997/1998, 8–13.
Abstract. The number of incoming calls for Emergency Road Service at AAA Michigan at
different times of a day differ significantly during winter and spring seasons. A regression model
is the bet, though weather, which is used as one of the independent variables, is difficult to
forecast more than a few days in advance. One the first day of a cold spell, call volumes are
usually very high which later on return to normal even though the temperatures are still very
low.
Keywords: Case studies, Associations, Forecasting techniques, Automobiles, Call centers, Customer
relations, Member services
12. Bianchi, Lisa, Jeffrey Jarrett and R. Choudary Hanumara. Improving forecasting for telemarketing
centers by ARIMA modeling with intervention, International Journal of Forecasting, 14
(4), 1998, 497–504.
Abstract. This study analyzes existing and improved methods for forecasting calls to telemarketing
centers for the purposes of planning and budgeting. The use of additive and multiplicative
versions of Holt-Winters exponentially weighted moving average models is analyzed and compared
to Box-Jenkins (ARIMA) modeling with intervention analysis. The forecasting accuracy
of HW and ARIMA models for samples of telemarketing data is determined.
Although there is much evidence in recent literature that simple models such as Holt-Winters
perform as well as or better than more complex models, it is found that ARIMA models with
intervention analysis perform better for the time series studied.
Keywords: Holt-Winters models, Intervention analysis, Box-Jenkins (ARIMA) modeling, Time
series
13. Faerber, J., S. Bodamer and J. Charzinski. Statistical evaluation and modeling of Internet dialup
traffic. Proceedings of the SPIE—The International Society for Optical Engineering, 3841,
1999, 112–121.
Abstract. In times of Internet access being a popular consumer application even for “normal”
residential users, some telephone exchanges are congested by customers using modem or ISDN
dial-up connections to their Internet service providers. In order to estimate the number of additional
lines and switching capacity required in an exchange or a trunk group, Internet access
traffic must be characterized in terms of holding time and call interarrival time distributions.
We analyze log files tracing the usage of the central ISDN access line pool at the University of
Stuttgart for a period of six months. Mathematical distributions are fitted to the measured data
and the fit quality is evaluated with respect to the blocking probability caused by the synthetic
traffic in a multiple server loss system. We show how the synthetic traffic model scales with the
number of subscribers and how the model could be applied to compute economy of scale results
for Internet access trunks or access servers.
Keywords: Statistical evaluation, Internet dial-up traffic, Traffic modeling, Holding-time distribution,
Consumer applications, Residential users, Telephone exchanges, Modem dial-up connec-
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