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FORECAST ERRORS IN SERVICE SYSTEMS

Published online by Cambridge University Press:  16 February 2009

Samuel G. Steckley ,
Shane G. Henderson  and
Vijay Mehrotra
Samuel G. Steckley
Affiliation:
The Mitre Corporation, McLean, VA 22102-7508 E-mail: sgsteckley@gmail.com
Shane G. Henderson
Affiliation:
School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY 14853 E-mail: sgh9@cornell.edu
Vijay Mehrotra
Affiliation:
Department of Decision Sciences, San Francisco State University, San Francisco, CA 94132-4156 E-mail: vjm@sfsu.edu
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Abstract

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We investigate the presence and impact of forecast errors in the arrival rate of customers to a service system. Analysis of a large dataset shows that forecast errors can be large relative to the fluctuations naturally expected in a Poisson process. We show that ignoring forecast errors typically leads to overestimates of performance and that forecast errors of the magnitude seen in our dataset can have a practically significant impact on predictions of long-run performance. We also define short-run performance as the random percentage of calls received in a particular period that are answered in a timely fashion. We prove a central limit theorem that yields a normal-mixture approximation for its distribution for Markovian queues and we sketch an argument that shows that a normal-mixture approximation should be valid in great generality. Our results provide motivation for studying staffing strategies that are more flexible than the fixed-level staffing rules traditionally studied in the literature.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 23 , Issue 2 , April 2009 , pp. 305 - 332
Copyright
Copyright © Cambridge University Press 2009

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