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predictive dialers and crm software
computer telephony software predictive dialer

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DSC Tech Library

Contact Center Equipment

telecommunications software solution 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:
Methods, Challenges, And Opportunities

Page 8

By Vijay Mehrotra, Department of Decision Sciences
College of Business - San Francisco State University

Jason Fama, Engineering Group, Blue Pumpkin Software Inc.


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 4:208-227.
    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 27:207-214.
    Mabert, V. A. 1985. Short-Interval Forecasting of Emergency (911) Workloads. Journal of Operations Management 5:259-271.
    Mandelbaum, A. 2001. Call Center Research Bibliography with Abstracts, Technical Report, Technion, Israel Institute of Technology.
    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 Today 24:18-24.
    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 31:87-101.
    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 Remedy.
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 framework.
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 .

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