DSC Tech Library
This section of our technical library presents information and documentation relating to Call Center technology and Best Practices plus software and products.
DSC is a leading provider of contact center technology and software solutions as well as predictive dialer phone systems for the modern call center. Customer contact center software includes CRM software and computer telephony integration solutions. These modern products help call center phone agents communicate effectively with your customers and prospects.
The following article presents product or service information relating to call centers and customer service help desks.
Call Center Simulation Modeling:
By Vijay Mehrotra, Department of Decision Sciences
Methods, Challenges, And Opportunities
College of Business - San Francisco State University
Jason Fama, Engineering Group, Blue Pumpkin Software Inc.
6 WHAT THE FUTURE HOLDS FOR CALL CENTER SIMULATION
Looking out into the future, we see two major trends impacting
call center simulation. First of all, operational
complexity will continue to grow: more queues, more different
agent schedules, more diverse skilling combinations
and routing rules. This will put pressure on analysts to not
only build richer models, but also to define output metrics
that enable them – and their management – to understand
the bigger picture as well as the more minute statistics.
Even in the very simple numerical example above, it is
easy to see how one can become overwhelmed with the
sheer volume of numbers that simulation can produce.
In addition, as executives begin to understand that the
call center is a key component in their customer value delivery
chain, we foresee an increased desire to understand
the risks inherent in any particular operational configuration.
In particular, we see interesting and important opportunities
in randomizing not only call arrival patterns and
handling times but also overall call volumes, and using
techniques from risk analysis and experimental design
along with simulation models to quantify system capacity
and delivery risks.
Finally, we hope for and expect improvements in the
quality of data provided for quantitative analysis. In particular,
increased accuracy and detail associated with handle
time distributions, waiting time distributions, and
abandonment time distributions will lead to better model
inputs and more robust results.
The authors would like to thank the call center directors,
managers, and executives that we have had the chance to
work with over the past several years. Through our professional
and personal interactions with these overworked and
underappreciated individuals, we have learned a great deal
about call center operations, management, and data
sources, all of which has contributed greatly to our ability
to model and analyze these types of systems. We have
also gotten a first-hand sense of the pressures that these individuals
work under, and hope that our experience and our
models can help provide insight and support to them.
Andrews, B. H. and S. M. Cunningham. 1995. L.L. Bean
Improves Call Center Forecasting. Interfaces 25:1-13.
Andrews, B. H. and H. L. Parsons. 1989. L.L. Bean Chooses
an Agent Scheduling System. Interfaces 19:1 – 9.
Andrews, B. H. and H. L. Parsons. 1993. Establishing
Telephone-Agent Staffing Levels Through Economic
Optimization. Interfaces 23:14-20.
Feinberg, R. A., I. Kim, B. Hokama, K. Ruyter, and C.
Keen. Operational Determinants of Caller Satisfaction
in the Call Center. International Journal of Service
Industry Management 11:131-141.
Garnett, O., A. Mandelbaum, and M. L. Reimann. 2002.
Designing a Call Center With Impatient Customers.
Manufacturing and Service Operations Management
Grossman, T. A., D. A. Samuelson, S. L. Oh, and T. R.
Rohleder. 2001. Call Centers. In Encyclopedia of Operations
Research, ed. S. L. Gass and T. M. Harris,
73-76. Norwell: Kluwer Academic Publishers.
Hoffman, K. L. and C. M. Harris. 1986. Estimation of a
Caller Retrial Rate for a Telephone Information System.
European Journal of Operational Research
Mabert, V. A. 1985. Short-Interval Forecasting of Emergency
(911) Workloads. Journal of Operations Management
Mandelbaum, A. 2001. Call Center Research Bibliography
with Abstracts, Technical Report, Technion, Israel Institute
Mandelbaum, A. and N. Shimkin. 2000. A Model for Rational
Abandonments from Invisible Queues. Queueing
Systems, Theory, and Application 36:141-173.
Mehrotra, V. 1997. Ringing Up Big Business. OR/MS
Pinker, E. and R. Shumsky. 2000. The Efficiency-Quality
Tradeoff of Crosstrained Workers. Manufacturing and
Service Operations Management 2:32-48.
Saltzman, R. and V. Mehrotra. 2001. A call center uses
simulation to drive strategic change. Interfaces
Samuelson, D. A. 1999. Predictive Dialing For Outbound
Telephone Call Centers. Interfaces 29:66-81.
VIJAY MEHROTRA is an Assistant Professor in the
College of Business at San Francisco State University.
Vijay joined the SFSU faculty in the fall of 2003 after
over ten years in the operations management consulting
field. Most recently, he was a Vice President with the Solutions
Group at Blue Pumpkin Software. Prior to joining
Blue Pumpkin, he was co-founder and CEO of Onward Inc.,
an operations management consulting firm based in Mountain View, CA that focuses on the successful application of
Operations Research techniques to business applications.
Vijay’s research interests include applications of stochastic
processes and optimization, queueing networks, and
the adoption of models and information technology by individuals
and organizations. Within the simulation area, he has
been actively involved with modeling semiconductor manufacturing
facilities, electric power production systems, container
ship traffic, as well as call center operations.
Over the course of his consulting career, Vijay has
worked with a wide variety of clients in many industries,
including EDS, Intuit, Hewlett Packard, Charles Schwab,
AOL, Hewlett Packard, IBM, General Electric, Sykes,
CIGNA, Tyecin Systems, National Semiconductor, and
Vijay holds a B.A. degree in Mathematics, Economics,
and Statistics from St. Olaf College and an M.S. and Ph.D.
in Operations Research from Stanford University. He is the
past President of the Northern California INFORMS chapter,
and writes a regular column in OR/MS Today entitled
“Was It Something I Said?” His email address is
JASON FAMA is an analyst and developer in the algorithms
group at Blue Pumpkin Software. While at Blue
Pumpkin, Jason has been actively involved in the development
of forecasting, queueing, and scheduling algorithms,
and with effectively embedding efficient simulation
models within Blue Pumpkin's overall workforce optimization
Prior to joining Blue Pumpkin, had worked as a researcher
Rockwell’s Palo Alto Laboratory. Jason holds a
B.S. degree in Economics and Computer Science from the
University of California at Berkeley. His email address is