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Estimating cross-training call center capacity through simulation

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Abstract

Call centers have grown world-wide during the past decade. One of the most important aspects considered by call center managers is the optimization of its operators, which implies covering the highly variable demand and finding an efficient way to assign people to certain shifts in order to achieve a desirable service level and abandonment rate. Another challenge is determining which system setup is appropriate for the specific call center. Should we have a single-skill call center or multi-skill call center? If we do have the latter, how many multi-skill agents should we have on staff? In this case study, we generate and analyze discrete-event systems simulation-optimization models to test the behavior of a real-world call center under the actual configuration and under different levels of cross-training. The model results help call center managers by: 1) determining the optimal number of operators needed for different staff configurations in order to achieve the targets for service level and abandonment; 2) providing information about the trade-off between the key measurements in the call center; and 3) providing useful information about the number of operators needed and used for each hour of operation to estimate the number of four-hour shifts required to achieve the performance targets. Our experimental findings from this case study suggest that a bi-skill call center is economically better in the long-run compared to a full-skill or single-skill call center. This case study augments the call center body of knowledge by providing additional managerial insights for the operations management community.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful and constructive comments that contributed to improving the final version of this paper.

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, State College, USA

    David A. Munoz & Nathaniel D. Bastian

Authors
  1. David A. Munoz
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  2. Nathaniel D. Bastian
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Corresponding author

Correspondence to David A. Munoz.

Additional information

David A. Munoz holds a Ph.D. in industrial engineering and operations research and an M.Eng. in industrial engineering from the Pennsylvania State University. Currently, he is a consultant in McKinsey and Company. His consulting and research interests include process improvement, social and behavioral network modeling, applied statistics, data-based policy making, and applied engineering decision making with applications in manufacturing, healthcare, education, and defense.

Nathaniel D. Bastian is a NSF graduate research fellow and Ph.D. candidate in industrial engineering and operations research in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at the Pennsylvania State University. He serves on the Adjunct Faculty in the graduate program in Predictive Analytics at Northwestern University, as well as the graduate program in data analytics at the City University of New York. He earned his M.Eng. in industrial engineering from Penn State, M.S. in econometrics and operations research from Maastricht University, and B.S. in engineering management with Honors from the U.S. Military Academy at West Point. His research interests include resource allocation optimization under uncertainty, productivity measurement and economic modeling, social network analysis and graph mining in complex networks, cost-effectiveness and econometric modeling, multiple criteria decision engineering, and predictive analytics with applications in healthcare delivery, national security and military operations, service systems, and logistics.

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Munoz, D.A., Bastian, N.D. Estimating cross-training call center capacity through simulation. J. Syst. Sci. Syst. Eng. 25, 448–468 (2016). https://doi.org/10.1007/s11518-015-5286-9

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  • DOI: https://doi.org/10.1007/s11518-015-5286-9

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