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Workforce Planning Framework for a Mobile Call Center Considering a Special Event

Author

Listed:
  • Thanyawan Chanpanit

    (Department of Production Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand)

  • Apinanthana Udomsakdigool

    (Department of Production Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand)

Abstract

Workforce planning is essential in today’s business management. If an organization can find and keep enough staff who have the right values, then they can provide high-quality service. This paper presents a workforce planning framework for selecting the best forecasting model in order to provide minimum wage and computer electricity costs for a mobile call center during the Songkran festival event, and to optimize workforce planning. The framework is constructed with four main steps: a study of a separate period; the separation of models with different data types; the simulation of models under different service levels to determine the number of customers waiting in a call center; and the evaluation of the models. The results from the proposed framework presented the best forecasting method and the optimal workforce plan. It is clear that this approach can assist in systematically selecting the best forecasting model. In addition, a workforce planner can use this framework to support workforce planning and cost evaluation in other event periods.

Suggested Citation

  • Thanyawan Chanpanit & Apinanthana Udomsakdigool, 2022. "Workforce Planning Framework for a Mobile Call Center Considering a Special Event," Energies, MDPI, vol. 15(4), pages 1-24, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1551-:d:753551
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    References listed on IDEAS

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    1. James W. Taylor, 2012. "Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing," Management Science, INFORMS, vol. 58(3), pages 534-549, March.
    2. Han Ye & Lawrence D. Brown & Haipeng Shen, 2020. "Hazard rate estimation for call center customer patience time," IISE Transactions, Taylor & Francis Journals, vol. 52(8), pages 890-903, August.
    3. Haipeng Shen & Jianhua Z. Huang, 2008. "Interday Forecasting and Intraday Updating of Call Center Arrivals," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 391-410, July.
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