Gatekeepers and Referrals in Services
Management Science
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Modeling Daily Arrivals to a Telephone Call Center
Management Science
Call center simulations: call center simulation modeling: methods, challenges, and opportunities
Proceedings of the 35th conference on Winter simulation: driving innovation
Analysis of call centre arrival data using singular value decomposition: Research Articles
Applied Stochastic Models in Business and Industry
Time series of count data: modeling, estimation and diagnostics
Computational Statistics & Data Analysis
Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach
Management Science
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science
Call Center Outsourcing Contract Analysis and Choice
Management Science
Interday Forecasting and Intraday Updating of Call Center Arrivals
Manufacturing & Service Operations Management
A Simple Staffing Method for Multiskill Call Centers
Manufacturing & Service Operations Management
On a Data-Driven Method for Staffing Large Call Centers
Operations Research
Analysis of operational data to improve performance in service delivery systems
Proceedings of the 8th International Conference on Network and Service Management
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A key input to the call center staffing process is a forecast for the number of calls arriving. Density forecasts of arrival rates are needed for analytical call center models, which assume Poisson arrivals with a stochastic arrival rate. Density forecasts of call volumes can be used in simulation models and are also important for the analysis of outsourcing contracts. A forecasting method, which has previously shown strong potential, is Holt--Winters exponential smoothing adapted for modeling the intraday and intraweek cycles in intraday data. To enable density forecasting of the arrival volume and rate, we develop a Poisson count model, with gamma distributed arrival rate, which captures the essential features of this exponential smoothing method. The apparent stationary level in our data leads us to develop versions of the new model for series with stationary levels. We evaluate forecast accuracy up to two weeks ahead using data from three organizations. We find that the stationary level models improve prediction beyond approximately two days ahead, and that these models perform well in comparison with sophisticated benchmarks. This is confirmed by the results of a call center simulation model, which demonstrates the use of arrival rate density forecasting to support staffing decisions. This paper was accepted by Yossi Aviv, operations management.