Workforce Calculation Using Erlang C Formula for Call Abandonment
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Solution Overview
Problem
Current methods for calculating workforce requirements in contact centers do not adequately account for actual operational environments and fail to optimize agent numbers based on call abandonment rates, leading to suboptimal service objectives and customer satisfaction.
Innovation Solution
A method using the Erlang C formula to calculate the probability of queuing and call abandonment rate, iteratively adjusting the number of operators to meet predetermined service objectives, considering factors like availability and absence rates, to determine the necessary workforce requirement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the theoretical M/M/c model is used to calculate the number of extensions, then the calculation can be performed, but the preconditions of the theoretical model are not fulfilled in actual operational environments
Solution Approach 1:
The patent changes the mathematical model parameters from the theoretical M/M/c model to the Erlang C formula, which better fits actual operational conditions. This parameter change allows the calculation to accurately reflect real-world scenarios where call abandonment occurs and queueing behavior varies, resolving the contradiction between ease of calculation and accuracy.
Solution Approach 2:
The patent implements an iterative calculation process where the number of operators is adjusted based on feedback from calculating the probability of queuing and call abandonment rate. This feedback mechanism ensures that the calculated operator numbers meet actual service objectives, improving reliability while maintaining computational feasibility.
2Productivity
If average queuing time is used as a service objective, then optimization can be performed, but many other factors must be considered and adjustment must be made using actual measured values
Solution Approach 1:
The patent changes the service objective parameter from average queuing time to call abandonment rate, which is a more critical and easier-to-measure metric. This parameter change simplifies the optimization process while maintaining productivity, as call abandonment rate directly reflects customer satisfaction and can be more reliably targeted.
Solution Approach 2:
The patent uses the Erlang C formula as a simplified model that copies the essential behavior of actual call center operations without requiring complex simulations or multiple measured parameters. This allows optimization to be performed using a single key metric (call abandonment rate) rather than multiple factors.
3Reliability
If the number of operators is increased to meet service objectives, then customer satisfaction improves, but the cost of operation increases
Solution Approach 1:
The patent implements a feedback-driven iterative process where the number of operators is calculated based on the desired call abandonment rate and actual call volume. This feedback mechanism ensures that the minimum necessary number of operators is identified to meet service objectives, avoiding unnecessary workforce increases and associated costs.
Solution Approach 2:
The patent changes the approach from fixed workforce allocation to dynamic calculation based on call volume and service objectives. By using the Erlang C formula to calculate the probability of queuing and call abandonment rate, the system determines the optimal number of operators needed, balancing customer satisfaction with operational efficiency.
Data Source
AI summary
A workforce requirement calculation device 10 accepts at least call volume in a particular period of time, an average handle time for a call by the operator, and a maximum queuing time before abandoning queuing of a call, as input parameters, initializes a number of the operators, calculates a probability of queuing of the call using the Erlang C formula based upon at least the call volume and the number of the operators, calculates a call abandonment rate as a service objective based upon at least the probability of queuing of the call and the input parameters, and calculates the workforce requirement for operators in order to meet a predetermined service objective by recurrently repeating the calculation of the probability of queuing of the call and the calculation of the call abandonment rate, with respect to an increase and a decrease in the number of the operators.