Workload forecasting for a call center: Methodology and a case study

TL;DR

This paper presents a new arrival count forecasting model for call centers using a mixed Poisson process, incorporating exogenous factors, and evaluates its accuracy for staffing decisions under the QED regime.

Contribution

It introduces a novel mixed Poisson process model for call arrivals that accounts for external events and assesses its practical performance for staffing optimization.

Findings

Model achieves high accuracy during most hours of the day.

Incorporating exogenous variables improves forecast precision.

Model supports effective staffing decisions under the QED regime.

Abstract

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.