Call Center Service Level Prediction via Discrete Event Simulation
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Solution Overview
Problem
Call centers face inefficiencies due to reactive management approaches, leading to inadequate staffing and skill mix during spikes in call volume, resulting in poor service levels and potential penalties.
Innovation Solution
An apparatus and method using a discrete event simulation model based on call data, agent skill, schedule, and attrition rates to predict future service levels and recommend optimal agent numbers and skill mixes for each queue, ensuring accurate staffing and cost control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive management approach is used, then current workload can be handled, but future spikes in call volume cannot be anticipated and staffed accordingly
Solution Approach 1:
The system performs preliminary actions by predicting future call volumes and service levels before they actually occur. The discrete event simulation model forecasts future scenarios and the optimization module determines required staffing levels in advance, allowing the call center to proactively adjust schedules and staffing rather than reacting after spikes occur.
Solution Approach 2:
The system implements feedback loops where actual service level data is continuously fed back into the simulation model to refine future predictions. The optimization module uses this feedback to adjust staffing recommendations, creating a closed-loop system that continuously improves accuracy in predicting and responding to call volume changes.
2Adaptability or versatility
If trial and error management is used, then flexibility is maintained, but cost control is not guaranteed
Solution Approach 1:
The system changes key parameters such as staffing levels, skill mix compositions, and shift schedules based on optimized predictions from the discrete event simulation model. The optimization module systematically adjusts these parameters to achieve the lowest possible cost while maintaining service level objectives, replacing trial-and-error with data-driven parameter optimization.
Solution Approach 2:
The system introduces dynamic staffing adjustments that can adapt to changing call volume patterns, seasonal variations, and skill availability. The optimization module continuously refines staffing configurations based on predicted future conditions, allowing the call center to dynamically adjust rather than using static or purely reactive staffing models.
3Reliability
If adequate staffing is provided for peak volumes, then service levels are maintained, but operational costs increase
Solution Approach 1:
The optimization module applies partial action by providing just enough staffing to meet predicted service level requirements rather than over-staffing for peak scenarios that may not occur. The discrete event simulation model allows the system to evaluate different staffing scenarios and select the minimum adequate staffing level that maintains service objectives while minimizing costs.
Solution Approach 2:
The system performs preliminary staffing calculations based on predicted future call volumes rather than reacting to actual peak demand. By forecasting future scenarios in advance and optimizing staffing levels beforehand, the system avoids both over-staffing and under-staffing, achieving the optimal balance between service level maintenance and cost control.
Data Source
AI summary
An apparatus, method and non-transitory computer readable medium for predicting a service level of a call center are disclosed. The method performs operations for predicting a service level of a call center. The operations include collecting call data, agent topic skill data, agent skill level data, agent schedule data and agent attrition rate data, building a discrete event simulation model based on the call data, the agent topic skill data, the agent skill level data, the agent schedule data and the agent attrition rate data, predicting the service level of the call center at a future time based on the discrete event simulation model and recommending a number of agents and a skill mix of agents for each queue in the call center at the future time based on the service level that is predicted based on the discrete event simulation model to achieve a call center service objective.