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Advanced workforce management for effective customer services

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  • Jun Kim
  • Sung Ha

Abstract

Because almost 60–80% of the total costs for operating a contact centre involve wage and benefit expenses for personnel, determining the optimal number of agents available is of great importance in call centre management. In modern call centres, working hours are divided into planning intervals with identical lengths. Each planning interval is typically assumed to be a homogeneous Poisson process in a steady state, and simple queuing models, such as Erlang-C (M/M/c), are often applied to determine the optimal staffing levels of the planning intervals. However, since the actual length of the planning interval in practice is relatively short, the basic assumption of staffing analysis could be violated. In this paper, we numerically analyze an M/M/c+M call centre’s behavior in a transient state. As a result, we can determine appropriate staffing levels of a call centre with short planning intervals which do not assume to be in a steady state. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Jun Kim & Sung Ha, 2012. "Advanced workforce management for effective customer services," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(6), pages 1715-1726, October.
    • Handle: RePEc:spr:qualqt:v:46:y:2012:i:6:p:1715-1726
    DOI: 10.1007/s11135-011-9554-6
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    References listed on IDEAS

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    1. Linda V. Green & Peter J. Kolesar & João Soares, 2001. "Improving the Sipp Approach for Staffing Service Systems That Have Cyclic Demands," Operations Research, INFORMS, vol. 49(4), pages 549-564, August.
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    2. 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.
    3. Eltigani, Dalia & Masri, Syafrudin, 2015. "Challenges of integrating renewable energy sources to smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 770-780.
    4. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    5. Gregor Selinka & Raik Stolletz & Thomas I. Maindl, 2022. "Performance Approximation for Time-Dependent Queues with Generally Distributed Abandonments," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 20-38, January.

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