Call centre capacity management

Call centre capacity management Call centres often experience large fluctuations in demand over relatively short periods of time. However, most centres also need to maintain short response times to the demand. This places great emphasis upon capacity management practices within call centre operations. A total of 12 UK-based call centres from one retail bank were studied to investigate how they managed forecasting, capacity management and scheduling tasks. Provides evidence of the difficulties associated with capacity management in call centres. Regression modelling is used to link forecasting and capacity planning practices to performance. Shows that random variation is a very important factor when assessing call centre performance. The results suggest that call centre managers can have only a small influence upon short-term performance. Existing mathematical models, such as the Erlang queuing system methodologies, have only limited value as the assumptions concerning demand patterns made in their derivation contradict observations made within the 12 sites. Spiked demand patterns present special capacity management problems, including a direct trade-off between high service levels and operator boredom. Conventional methods of flexing capacity cannot respond sufficiently well to some of the short-term fluctuations in demand. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Service Industry Management Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0956-4233
DOI
10.1108/09564230010323840
Publisher site
See Article on Publisher Site

Abstract

Call centres often experience large fluctuations in demand over relatively short periods of time. However, most centres also need to maintain short response times to the demand. This places great emphasis upon capacity management practices within call centre operations. A total of 12 UK-based call centres from one retail bank were studied to investigate how they managed forecasting, capacity management and scheduling tasks. Provides evidence of the difficulties associated with capacity management in call centres. Regression modelling is used to link forecasting and capacity planning practices to performance. Shows that random variation is a very important factor when assessing call centre performance. The results suggest that call centre managers can have only a small influence upon short-term performance. Existing mathematical models, such as the Erlang queuing system methodologies, have only limited value as the assumptions concerning demand patterns made in their derivation contradict observations made within the 12 sites. Spiked demand patterns present special capacity management problems, including a direct trade-off between high service levels and operator boredom. Conventional methods of flexing capacity cannot respond sufficiently well to some of the short-term fluctuations in demand.

Journal

International Journal of Service Industry ManagementEmerald Publishing

Published: May 1, 2000

Keywords: Service operations; Banking; Call centres

References

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