Enhancing Call Center Efficiency: Data Driven Workload Prediction and Workforce Optimization

Abstract

Organizations can improve customer service quality, reduce wait times, and enhance overall operational efficiency by aligning staffing levels with predicted workload volume. Decision makers in the call centers gain valuable insights and practical guidance from the integration of workload forecasting and workforce optimization. Businesses can effectively utilize their personnel and resources by accurate workload forecasting and workforce optimization. Faster and more profitable services can be provided at customer contact points. It also increases employee satisfaction and enhances the organization's competitive advantage. A tailored solution is essential because every issue has its distinct dynamics. The two-layered pipeline known as "Predict and Optimize" is created by combining ML approaches for forecasting and mathematical programming techniques for optimization. The method offers a comprehensive solution for call center managers seeking to improve resource allocation and boost operational performance. In this study, we have tried to predict future workload levels by training a LSTM model and used integer programming techniques to optimize the allocation of available staff resources according to the forecasted workload. The workforce optimization model generates minimum staffing requirements by considering call center-specific various constraints.

Keywords

• Predict and optimize
• Time series
• Integer programming

References

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