What is the purpose of outlier detection in the automatic best method?

The purpose of outlier detection in the automatic best method is fundamentally to identify and correct forecasting errors. Outlier detection is a statistical approach that helps identify data points that significantly deviate from the expected pattern. In workforce management, this is particularly relevant to forecasting demand for services or workloads.

By identifying outliers, organizations can pinpoint anomalies in historical data that may indicate miscalculations, unexpected events, or irregular patterns in customer interactions. Correcting these errors is crucial for creating more accurate forecasts, which in turn helps in better resource planning and staffing decisions. Accurate forecasting is essential for maintaining efficiency, improving service levels, and ensuring that agents are optimally scheduled based on anticipated demand. Thus, the role of outlier detection directly ties into refining forecasting processes, leading to better overall management of workforce resources.