ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

QED Queues: Quality- and Efficiency-Driven Call Centers

23 September 2008, 09:20 – 09:40

Abstract

Submitted by
Avi Mandelbaum
Authors
Avishai (Avi) Mandelbaum
Affiliation
Industrial Engineering & Management, Technion; http://ie.technion.ac.il/serveng
Abstract
Through examples of Service Operations, with a focus on Telephone Call Centers, I review
empirical findings that motivate or are motivated by (or both) interesting research questions. These
findings give rise to features that are prerequisites for useful service models, for example customers’
(im)patience, time-varying demand, heterogeneity of customers and servers, over-dispersion in Poisson
arrivals, generally-distributed (as opposed to exponential) service- and patience-durations, and
more. Empirical analysis also enables validation of existing models and protocols, either supporting
or refuting their relevance and robustness.
The mathematical framework for my models is asymptotic queueing theory, where limits
are taken as the number of servers increases indefinitely, in a way that maintains a delicate balance
against the offered-load. Asymptotic analysis reveals an operational regime that achieves, under
already moderate scale, remarkably high levels of both service quality and efficiency. This is the
QED Regime, discovered by Erlang and characterized by Halfin & Whitt. (QED = Quality- and
Efficiency-Driven).
My main data-source is a unique repository of call-centers data, designed and maintained at
the Technion’s SEE Laboratory. (SEE = Service Enterprise Engineering). The data is unique in
that it is transaction-based: it details the individual operational history of all the calls handled by
the participating call centers. (For example, one source of data is a network of 4 call centers of a
U.S. bank, spanning 2.5 years and covering about 1000 agents; there are 218,047,488 telephone calls
overall, out of which 41,646,142 where served by agents, while the rest were handled by answering
machines.) To support data analysis, a universal data-structure and a friendly interface have been
developed, under the logo DataMOCCA = Data MOdels for Call Centers Analysis. (I shall have
with me DataMOCCA DVD’s for academic distribution.)
Background Reading
1. Gans, N., Koole, G., Mandelbaum, A. “Telephone Call Centers: Tutorial, Review and Research
Prospects.” Invited review paper by Manufacturing and Service Operations Management
(M&SOM), 5 (2), 79141, 2003.
http://iew3.technion.ac.il/serveng/References/Gans-Koole-Mandelbaum-CCReview.pdf
2. Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Zeltyn, S., Zhao, L. and Haipeng, S.
“Statistical Analysis of a Telephone Call Center : A Queueing-Science Perspective.” Journal
of the American Statistical Association (JASA), 100, 36-50, 2005.
http://iew3.technion.ac.il/serveng/References/JASA callcenter.pdf

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