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Staffing to Maximize Profit for Call Centers with Alternate Service-Level Agreements

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  • Opher Baron

    (Rotman School of Management, University of Toronto, Toronto, Ontario, Canada M5S 3E6)

  • Joseph Milner

    (Rotman School of Management, University of Toronto, Toronto, Ontario, Canada M5S 3E6)

Abstract

To ensure quality from outsourced call centers, firms sign service-level agreements (SLAs). These define service measures such as what constitutes an acceptable delay or an acceptable abandonment rate. They may also dictate penalties for failing to meet agreed-upon targets. We introduce a period-based SLA that measures performance over a short duration such as a rush hour. We compare it to alternate SLAs that measure service by individual and over a long horizon. To measure the service levels for these SLAs, we develop several approximations. We approximate the probability an acceptable delay is met by generalizing the heavy-traffic quality and efficiency driven regime. We also provide a new approximation for the abandonment rate. Further, we prove a central limit theorem for the probability of meeting a service level measured by the percentage of customers acceptably served during a period. We demonstrate how an outsourced call center operating in an environment with uncertain demand and abandonment can determine its staffing policy to maximize the expected profit for these SLAs. Numerical experiments demonstrate a high degree of accuracy for the approximations and the resulting staffing levels. We indicate several salient features of the behavior of the period-based SLA.

Suggested Citation

  • Opher Baron & Joseph Milner, 2009. "Staffing to Maximize Profit for Call Centers with Alternate Service-Level Agreements," Operations Research, INFORMS, vol. 57(3), pages 685-700, June.
  • Handle: RePEc:inm:oropre:v:57:y:2009:i:3:p:685-700
    DOI: 10.1287/opre.1080.0585
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    References listed on IDEAS

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    1. Mor Armony & Avishai Mandelbaum, 2011. "Routing and Staffing in Large-Scale Service Systems: The Case of Homogeneous Impatient Customers and Heterogeneous Servers," Operations Research, INFORMS, vol. 59(1), pages 50-65, February.
    2. Jerome Niyirora & Jamol Pender, 2016. "Optimal staffing in nonstationary service centers with constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(8), pages 615-630, December.
    3. Zhou, Liping & Geng, Na & Jiang, Zhibin & Wang, Xiuxian, 2017. "Combining revenue and equity in capacity allocation of imaging facilities," European Journal of Operational Research, Elsevier, vol. 256(2), pages 619-628.
    4. Noa Zychlinski, 2023. "Applications of fluid models in service operations management," Queueing Systems: Theory and Applications, Springer, vol. 103(1), pages 161-185, February.
    5. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    6. Avishai Mandelbaum & Sergey Zeltyn, 2009. "Staffing Many-Server Queues with Impatient Customers: Constraint Satisfaction in Call Centers," Operations Research, INFORMS, vol. 57(5), pages 1189-1205, October.
    7. Opher Baron & Oded Berman & Dmitry Krass & Jianfu Wang, 2014. "Using Strategic Idleness to Improve Customer Service Experience in Service Networks," Operations Research, INFORMS, vol. 62(1), pages 123-140, February.
    8. Alex Roubos & Ger Koole & Raik Stolletz, 2012. "Service-Level Variability of Inbound Call Centers," Manufacturing & Service Operations Management, INFORMS, vol. 14(3), pages 402-413, July.
    9. Francis de Véricourt & Otis B. Jennings, 2011. "Nurse Staffing in Medical Units: A Queueing Perspective," Operations Research, INFORMS, vol. 59(6), pages 1320-1331, December.
    10. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    11. Shuangchi He, 2020. "Diffusion Approximation for Efficiency-Driven Queues When Customers Are Patient," Operations Research, INFORMS, vol. 68(4), pages 1265-1284, July.
    12. Ying Chen & John J. Hasenbein, 2017. "Staffing large-scale service systems with distributional uncertainty," Queueing Systems: Theory and Applications, Springer, vol. 87(1), pages 55-79, October.
    13. Opher Baron & Oded Berman & Dmitry Krass & Jianfu Wang, 2017. "Strategic Idleness and Dynamic Scheduling in an Open-Shop Service Network: Case Study and Analysis," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 52-71, February.
    14. Christopher Dance & Alexei Gaivoronski, 2012. "Stochastic optimization for real time service capacity allocation under random service demand," Annals of Operations Research, Springer, vol. 193(1), pages 221-253, March.

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