Forecasting Call Center Arrivals: Fixed-Effects, Mixed-Effects, and Bivariate Models

Rouba Ibrahim () and Pierre L'Ecuyer ()
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Rouba Ibrahim: Department of Management Science and Innovation, University College London, London WC1E 6BT, United Kingdom
Pierre L'Ecuyer: Department of Computer Science and Operations Research, University of Montreal, Montreal, Quebec H3C 3J7, Canada

Manufacturing & Service Operations Management, 2013, vol. 15, issue 1, 72-85

Abstract: We consider different statistical models for the call arrival process in telephone call centers. We evaluate the forecasting accuracy of those models by describing results from an empirical study analyzing real-life call center data. We test forecasting accuracy using different lead times, ranging from weeks to hours in advance, to mimic real-life challenges faced by call center managers. The models considered are (i) a benchmark fixed-effects model that does not exploit any dependence structures in the data; (ii) a mixed-effects model that takes into account both interday (day-to-day) and intraday (within-day) correlations; and (iii) two new bivariate mixed-effects models, for the joint distribution of the arrival counts to two separate queues, that exploit correlations between different call types. Our study shows the importance of accounting for different correlation structures in the data.

Keywords: forecasting; arrival process; dynamic updating; correlation; call centers (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:15:y:2013:i:1:p:72-85

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