Contact Center Staffing Shrinkage Forecasting from Agent Time-Offs
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Solution Overview
Problem
Inaccurate staffing-shrinkage calculations lead to understaffing or overstaffing in contact centers, resulting in inefficiencies, increased costs, and reduced customer service levels, making it difficult to optimize scheduling and resource allocation.
Innovation Solution
A computerized method and system that predicts staffing-shrinkage percentage in a time-slot during a future staffing plan by calculating historic-shrinkage based on agent time-offs and net-staffing data, using Generative Artificial Intelligence (GenAI) to update the staffing level in the WFM application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If staffing-shrinkage is not considered or miscalculated, then labor costs may be reduced through overstaffing avoidance, but customer service levels drop due to understaffing
Solution Approach 1:
The system performs preliminary calculation of staffing-shrinkage percentages based on historical data before generating the staffing plan. This advance calculation allows the system to proactively adjust staffing levels to account for expected shrinkage, ensuring adequate coverage during peak periods while avoiding both understaffing and overstaffing scenarios.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual shrinkage against predicted shrinkage and using this information to refine future staffing plans. The system adjusts staffing levels dynamically based on performance data, creating a closed-loop system that improves accuracy over time and maintains service levels while optimizing labor costs.
2Measurement precision
If accurate staffing-shrinkage calculations are implemented, then forecast accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically calculating staffing-shrinkage percentages using its own historical data without requiring external manual input. The system autonomously retrieves historical shrinkage data, performs calculations, and integrates results into staffing plans, reducing the need for complex manual processes while maintaining high forecast accuracy.
Solution Approach 2:
The system segments the staffing-shrinkage calculation into distinct components: retrieving historical data, calculating shrinkage percentages by time period, adjusting for seasonal variations, and integrating into overall staffing plans. This modular approach simplifies the system architecture while enabling sophisticated accuracy through multiple calculation stages.
3Measurement precision
If historical data analysis is performed to determine staffing-shrinkage, then prediction accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary aggregation and organization of historical shrinkage data during off-peak periods, preparing summary statistics and trends in advance. This pre-processing allows the system to quickly retrieve pre-computed values during staffing plan generation, maintaining high prediction accuracy while minimizing real-time processing delays.
Solution Approach 2:
The system dynamically adjusts the granularity of historical data analysis based on the specific forecasting needs. For routine forecasts, it uses aggregated monthly or weekly shrinkage averages for faster processing. For exceptional circumstances or detailed analysis, it can access more granular daily or hourly data, optimizing the balance between accuracy and processing time.
Data Source
AI summary
A computerized-method for predicting a staffing-shrinkage percentage in a time-slot during a future staffing plan in a contact center, The computerized-method includes: (i) receiving via a User Interface that is associated to a Workforce Management (WFM) application, skills for the future staffing plan and the time-slot; (ii) calculating a historic-shrinkage percentage during a preconfigured period based on agents time-offs and understaffing-levels during the preconfigured period; (iii) determining the staffing-shrinkage percentage in the time-slot during the future staffing plan based on the calculated historic-shrinkage percentage; and (iv) configuring the WFM application to update staffing level in the future staffing plan, based on the determined staffing-shrinkage percentage.