Forecast errors in service systems. Steckley, S. G., Henderson, S. G., & Mehrotra, V. Probability in the Engineering and Informational Sciences, 23(2):305–332, 2009. ![Forecast errors in service systems [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex 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.
@article{stehenmeh06,
abstract = {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.},
author = {Samuel G. Steckley and Shane G. Henderson and Vijay Mehrotra},
date-added = {2016-01-10 16:07:54 +0000},
date-modified = {2016-01-10 16:07:54 +0000},
journal = {Probability in the Engineering and Informational Sciences},
number = {2},
pages = {305--332},
title = {Forecast errors in service systems},
url_paper = {pubs/SteHenMeh09.pdf},
volume = {23},
year = {2009}}
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