GUEST LECTURER
Professor Shane Henderson

AFFILIATION
Cornell University

ABSTRACT
Joint work with Sam Steckley and Vijay Mehrotra

We investigate the presence and impact of forecast errors in the arrival rate of customers to a service system. Analysis of a large data set 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 over-estimates of performance, and that forecast errors of the magnitude seen in our data set 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 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.