Forecasting Hourly Peak Call Volume for a Rural Electric Cooperative Call Center

Kim, Taeyoon; Kenkel, Phil; Brorsen, B. Wade

doi:10.1002/for.1226

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…Finally, in certain types of call centers, for example where people may call to report power outages or those designated to emergency services, bursts of high arrival rates over short periods of time do occur. In this context, an important accident may trigger several dozen different calls within a few minutes, all related to the same event, resulting in a much larger than expected number of calls during that time frame; e.g., see Kim et al (2012) for the modeling of peak periods in a rural electric cooperative call center.…”

Section: Key Properties Of Call Center Arrival Processesmentioning

confidence: 99%

“…These models account for the dependence between the two call types by assuming that the vectors of random effects or the vectors of residuals across call types are correlated multinormal. This corresponds to using a normal copula; see Kim et al (2012). The choice of copula can have a significant impact on performance measures in call centers, because of the strong effect of tail dependence on the quality of service Jaoua et al (2013).…”

Section: Models Over Several Daysmentioning

confidence: 99%

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Modeling and forecasting call center arrivals: A literature survey and a case study

Ibrahim

L’Ecuyer

et al. 2016

International Journal of Forecasting

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The effective management of call centers is a challenging task mainly because managers are consistently facing considerable uncertainty. Among important sources of uncertainty are call arrival rates which are typically time-varying, stochastic, dependent across time periods and across call types, and often affected by external events. Accurately modeling and forecasting future call arrival volumes is a complicated issue which is critical for making important operational decisions, such as staffing and scheduling, in the call center. In this paper, we review the existing literature on modeling and forecasting call arrivals. We also discuss the key issues in building good statistical arrival models. Additionally, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data. We conclude by summarizing future research directions in this important field.

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Section: Key Properties Of Call Center Arrival Processesmentioning

confidence: 99%

Section: Models Over Several Daysmentioning

confidence: 99%

Modeling and forecasting call center arrivals: A literature survey and a case study

Ibrahim

L’Ecuyer

et al. 2016

International Journal of Forecasting

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show abstract

“…Again, the dependence here can be modeled via a copula, after fitting the marginals individually. The simplest and more practical type of copula for this is probably the normal copula, used for example by Kim, Kenkel, and Brorsen (2012) and Ibrahim and L'Ecuyer (2012). However, empirical data suggests that for certain pairs of call types, the coefficient of upper or lower tail dependence, which measures the strength of the dependence in the right or left tail of the distribution, is quite different from that implied by a normal copula.…”

Section: Modeling Arrivals Over a Single Daymentioning

confidence: 99%

On the modeling and forecasting of call center arrivals

Ibrahim

Han

L’Ecuyer

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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We review and discuss the key issues in building statistical models for the call arrival process in telephone call centers, and then we survey and compare various types of models proposed so far. These models are used both for simulation and to forecast incoming call volumes to make staffing decisions and build (or update) work schedules for agents who answer those calls. Commercial software and call center managers usually base their decisions solely on point forecasts, given in the form of mathematical expectations (conditional on current information), but distributional forecasts, which come in the form of (conditional) probability distributions, are generally more useful, in particular in the context of simulation. Building realistic models is not simple, because arrival rates are themselves stochastic, time-dependent, dependent across time periods and across call types, and are often affected by external events. As an illustration, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data.

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“…More generally, our paper belongs to the literature dealing with forecasts of daily call volumes (Andrews and Cunningham 1995;Antipov and Meade 2002;Bianchi et al 1998;Mabert 1985), but is also related with more recent studies focusing on density and intra-day predictions (Kim et al 2012;Taylor 2008Taylor , 2012Tych et al 2002;Weinberg et al 2007). 1 The plan of the paper is as follows.…”

Section: Introductionmentioning

confidence: 99%

Statistical and economic evaluation of time series models for forecasting arrivals at call centers

2018

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Call centers' managers are interested in obtaining accurate point and distributional forecasts of call arrivals in order to achieve an optimal balance between service quality and operating costs. We present a strategy for selecting forecast models of call arrivals which is based on three pillars: (i) flexibility of the loss function; (ii) statistical evaluation of forecast accuracy; and (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of daily call arrivals. Although we focus mainly on point forecasts, we also analyze density forecast evaluation. We show that second-moment modeling is important for both point and density forecasting and that the simple seasonal random walk model is always outperformed by more general specifications. Our results suggest that call center managers should invest in the use of forecast models which describe both first and second moments of call arrivals.

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Forecasting Hourly Peak Call Volume for a Rural Electric Cooperative Call Center

Cited by 8 publications

References 13 publications

Modeling and forecasting call center arrivals: A literature survey and a case study

Modeling and forecasting call center arrivals: A literature survey and a case study

On the modeling and forecasting of call center arrivals

Statistical and economic evaluation of time series models for forecasting arrivals at call centers

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