Optimization of a Call Centre Performance Using the Stochastic Queueing Models

• Authors: Alenka Brezavšček , Alenka Baggia
• Languages of publication: EN

Abstracts

EN

Background A call centre usually represents the first contact of a customer with a given company. Therefore, the quality of its service is of key importance. An essential factor of the call centre optimization is the determination of the proper number of operators considering the selected performance measure. Results of previous research show that this can be done using the queueing theory approach. Objectives: The paper presents the practical application of the stochastic queueing models aimed at optimizing a Slovenian telecommunication provider’s call centre. Methods/Approach: The arrival and the service patterns were analysed, and it was concluded that the call centre under consideration can be described using the M/M/r {infinity/infinity/FIFO} queueing model. Results: An appropriate number of operators were determined for different peak periods of the working day, taking into consideration the following four performance measures: the expected waiting time, the expected number of waiting customers, the probability that a calling customer will have to wait, and the call centre service level. Conclusions: The obtained results prove the usefulness and applicability of the queueing models as a tool for a call centre performance optimization. In practice, all the data needed for such a mathematical analysis are usually provided. This paper is aimed at illustrating how such data can be efficiently exploited.

Keywords

EN

call centre; service quality; performance measure; optimization; stochastic queueing models

• Publisher: Sciendo
• Journal: Business Systems Research Journal
• Year: 2014
• Volume: 5
• Issue: 3
• Pages: 6-18

Dates

published

2014-09-01

received

2014-01-20

accepted

2014-06-18

online

2014-09-25

Contributors

author

Alenka Brezavšček

• Faculty of Organizational Sciences, University of Maribor, Slovenia

author

Alenka Baggia

• Faculty of Organizational Sciences, University of Maribor, Slovenia

References

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Identifiers

DOI

10.2478/bsrj-2014-0016