CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

Keywords: Multiserver exponential queues, Abandonments, Nash equilibrium, Call centers
(Appears also in Section III.)
92. Mockus, Jonas. “Call centers” in A Set of Examples of Global and Discrete Optimization: Applications,
Jonas Mockus (Editor). Dordrecht: Kluwer Academic, 2000, part 3, ch. 15.
93. Pinedo, Michael L., Sridhar Seshadri and J. George Shanthikumar. Call centers in financial services:
Strategies, technologies and operations, in Creating Value in Financial Services: Strategies,
Operations, and Technologies, Edward L. Melnick, Praveen R. Nayyar, Michael L. Pinedo,
Sridhar Seshadri (Eds.). Boston: Kluwer Academic Publishers, 2000, Chapter 18: 357–388.
Abstract. Call centers are becoming more important in financial services. They are of importance
to retail banking operations, credit card operations and mutual fund organizations. A
significant part of the dynamics of call centers in financial services is similar to call centers in
other industries. Analyzing both static and dynamic aspects of managing call centers, we discuss
necessary service, security and database requirements for call centers in financial services firms.
We also analyze the differences between call centers in financial services and call centers in other
industries such as airlines. These differences center around the more extensive database requirements
necessary to handle each call, as well as the fact that customers of financial institutions
tend to be more captive than customers of airlines.
Acknowledgement: The abstract was taken from the introduction of the book.
94. Pinker, E. and R. Shumsky. The efficiency-quality tradeoff of cross-trained workers, Manufacturing
and Service Operations Management, 2 (1), Winter 2000, 32–48.
Abstract. Does cross-training workers allow a firm to achieve economies of scale when there is
variability in the content of work, or does it create a work force that performs many tasks with
consistent mediocrity? To address this question we integrate a model of a stochastic service system
with models for tenure- and experience-based service quality. When examined in isolation,
the service system model confirms a well-known “rule of thumb” from the queueing literature:
Flexible or cross-trained servers provide more throughput with fewer workers than specialized
servers. However, in the integrated model these economies of scale are tempered by a loss in
quality. Given multiple tasks, flexible workers may not gain sufficient experience to provide
high-quality service to any one customer, and what is gained in efficiency is lost in quality.
Through a series of numerical experiments we find that low utilization in an all-specialist system
can also reduce quality, and therefore, the optimal staff mix combines flexible and specialized
workers. We also investigate when the performance of the system is sensitive to the staffing
configuration choice. For small systems with high learning rates, the optimal staff mix provides
significant benefits over either extreme case (a completely specialized or completely flexible work
force). If the system is small and the rate of learning is slow, flexible servers are preferred. For
large systems with high learning rates, the model leans toward specialized servers. In a final set
of experiments, the model analyzes the design options for an actual call center.
Keywords: Queues: Approximations, Service quality, Learning curves, Cross-training, Worker
turnover; Personnel
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