Collaborative Research: Robust Strategies for Cross-Training Call Center Agents - Taxonomy, Models, and Analysis

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This research on strategies for cross-training and call/agent assignment is a ripe research topic that promises not only scientific innovation, but also a significant step forward in call center managerial practice and performance. This research has the potential to impact call center agents through increased career development and quality of life and help organizations with call centers through improved practices that lead to improved profitability. Moreover, it will increase the quality of service experienced by the users of call centers, which includes nearly the entire population. Within the last decade, call centers have become a large service industry employing roughly 3-4 million Americans, growing at about 10% annually, according to Data Monitor. The operational management of call centers, which is a notoriously difficult task, has developed to the point where the technology is already available to dynamically route incoming calls to the most suitable customer service representative (CSR), or agent, based upon their skills and training. Much more than convenience and profit are at stake. Critical emergency services such as 911, police, ambulance, and fire dispatching depend upon call centers and have experimented with cross-training call center agents to handle multiple call types. In response to these pressing needs, this project develops innovative approaches to setting effective strategies for determining which agents to cross-train for more than one task as well as how to best assign calls to them. The principal investigators have interacted with industrial call center managers and software solution providers to maximize the impact of this workThis research will construct a detailed, conceptual classification scheme for call center environments that identifies key characteristics germane to the selection of a cross training strategy. It will create and analyze a series of mathematical models that predict the performance of various cross-training patterns utilizing skills-based call routing and provide insight into the factors that determine their efficacy from a cost/benefit perspective as well as the system's response performance. The analysis will use tools that include queuing theory, Markov decision processes, discrete event systems theory, and simulation. The anticipated results of this research are: (1) managerial insights that greatly deepen the understanding of which systems will benefit from cross-training and a suitable strategy for implementation; (2) CSR (Customer Service Representative) cross-training strategies that are robustly effective across a wide range of call centers; (3) useful analytical models for the analysis and design of agile work systems; and (4) extensions of the queuing technology base to include broad classes of systems where servers operate in new and complex ways based on their skill sets. Upon implementation, the results will impact users of call centers with increased quality of service, agents through increased career development and quality of life, and firms (small, medium, and large call centers) through improved management practices.