Call Centre Simulation - Modelling and optimising call operations

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22%

Reduction in average wait time

15%

Improved agent utilisation

3%

Reduction in call abandonment rate

Client
A leading media and telecom company, offering television, broadband, mobile, and streaming services to millions of customers across the UK and Europe.
Industry
Telecom
Company Size
10,000+
Location
London, United Kingdom
Project Duration
4 months (Feb 2024 - Jun 2024)
Framework
  • Python
  • SimPy
  • Altair
  • Streamlit
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Simulated end-to-end call centre operations using discrete event modelling to optimise staffing, queue logic, and service levels. The model incorporated realistic arrival patterns and agent handling times to test performance under varying demand.

Efficient call center operations are critical for balancing customer satisfaction and operational cost. In this project, I developed a discrete event simulation to replicate the behaviour of a real-world call center, accounting for factors such as customer arrival rates, service durations, wait time tolerances, agent shifts, and prioritisation rules.

The simulation allowed us to test how different configurations would perform under realistic and extreme conditions. I experimented with changes in call volume, agent availability, and queueing strategies to measure their impact on key performance indicators like average wait time, call abandonment rate, and agent utilisation.

This model enabled data-driven decision-making by identifying staffing thresholds that maintain service levels during peak hours while reducing unnecessary overhead during quiet periods. It also offered insights into how queueing logic (e.g., priority queues or overflow handling) could improve system efficiency and reduce customer frustration. The final model became a prototype for evaluating future policy changes without disrupting live operations.

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