Call Center Arrivals: When to Jointly Forecast Multiple Streams?

We consider call centers that have multiple (potentially inter‐dependent) demand arrival streams. Workforce management of such labor intensive service systems starts with forecasting future arrival demand. We investigate the question of whether and when to jointly forecast future arrivals of the multiple streams. We first develop a general statistical model to simultaneously forecast multi‐stream arrival rates. The model takes into account three types of inter‐stream dependence. We then show with analytical and simulation studies how the forecasting benefits of the multi‐stream forecasting model vary by the type, direction, and strength of inter‐stream dependence. In particular, we find that it is beneficial to simultaneously forecast multi‐stream arrivals (instead of separately forecasting each stream), when there exists inter‐stream lag dependence among daily arrival rates. Empirical studies, using two real call center datasets further demonstrate our findings, and provide operational insights into how one chooses forecasting models for multi‐stream arrivals.

[1]  Vincent A. Mabert,et al.  Short interval forecasting of emergency phone call (911) work loads , 1985 .

[2]  Mathew W. McLean,et al.  Forecasting emergency medical service call arrival rates , 2011, 1107.4919.

[3]  Haipeng Shen,et al.  Interday Forecasting and Intraday Updating of Call Center Arrivals , 2008, Manuf. Serv. Oper. Manag..

[4]  Avishai Mandelbaum,et al.  Statistical Analysis of a Telephone Call Center , 2005 .

[5]  Pierre L'Ecuyer,et al.  Modeling Daily Arrivals to a Telephone Call Center , 2003, Manag. Sci..

[6]  Harrison H. Zhou,et al.  The root–unroot algorithm for density estimation as implemented via wavelet block thresholding , 2010 .

[7]  Haipeng Shen,et al.  Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling , 2015, Manuf. Serv. Oper. Manag..

[8]  James W. Taylor,et al.  Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing , 2012, Manag. Sci..

[9]  James W. Taylor,et al.  A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center , 2008, Manag. Sci..

[10]  Haipeng Shen,et al.  Functional dynamic factor models with application to yield curve forecasting , 2012, 1209.6172.

[11]  Avishai Mandelbaum,et al.  Workload forecasting for a call center: Methodology and a case study , 2009, 1009.5741.

[12]  Jonathan Weinberg,et al.  Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data , 2007 .

[13]  Avishai Mandelbaum,et al.  Telephone Call Centers: Tutorial, Review, and Research Prospects , 2003, Manuf. Serv. Oper. Manag..

[14]  Ward Whitt,et al.  Are Call Center and Hospital Arrivals Well Modeled by Nonhomogeneous Poisson Processes? , 2014, Manuf. Serv. Oper. Manag..

[15]  Haipeng Shen,et al.  FORECASTING TIME SERIES OF INHOMOGENEOUS POISSON PROCESSES WITH APPLICATION TO CALL CENTER WORKFORCE MANAGEMENT , 2008, 0807.4071.

[16]  Pierre L'Ecuyer,et al.  Forecasting Call Center Arrivals: Fixed-Effects, Mixed-Effects, and Bivariate Models , 2012, Manuf. Serv. Oper. Manag..

[17]  Zeynep Akşin,et al.  The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research , 2007 .

[18]  Pierre L'Ecuyer,et al.  Modeling and forecasting call center arrivals: A literature survey and a case study , 2015 .

[19]  Robert D. van der Mei,et al.  On the estimation of the true demand in call centers with redials and reconnects , 2015, Eur. J. Oper. Res..