Weighted Advance Time Formula for Call Center Resource Allocation
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Solution Overview
Problem
Existing resource allocation systems in call centers fail to accurately reflect desired service level targets due to assumptions that all agents contribute equally to servicing queues, leading to inefficient allocation of resources based on predicted wait times.
Innovation Solution
A new formula for Weighted Advance Time (WAT) is introduced, which takes into account the prior contribution of agents to specific queues, updating WAT based on the average agent contribution to service work, ensuring more accurate allocation of resources to enqueued requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If resource allocation is based on equal agent contribution assumptions, then the allocation process is simple, but the service level targets are not accurately reflected
Solution Approach 1:
The patent changes the parameter used for resource allocation from a uniform equal-contribution assumption to individualized historical contribution metrics. Each agent's past performance data is captured and used to calculate personalized Weighted Advance Times, transforming the allocation basis from generic to specific, thereby improving service level target accuracy without significantly increasing system complexity
Solution Approach 2:
The system implements feedback by continuously monitoring and recording each agent's historical contribution to different queues. This feedback loop allows the resource allocation system to learn from past performance patterns and adjust future allocations accordingly, ensuring that service level targets are accurately reflected in the allocation decisions
2Speed
If traditional Weighted Advance Time calculation is used, then allocation decisions are made quickly, but resource allocation efficiency is suboptimal
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing each agent's historical contribution metrics and average service times for different queues before allocation decisions are needed. When an allocation decision is required, the system simply retrieves these pre-computed values to calculate Weighted Advance Time, maintaining fast decision speed while incorporating detailed efficiency information
Solution Approach 2:
The system changes the parameters used in Weighted Advance Time calculation from generic queue metrics to agent-specific historical performance parameters. By incorporating individual agent contribution data and queue-specific service patterns, the calculation becomes more sophisticated yet remains computationally efficient through the use of pre-aggregated statistics
Data Source
AI summary
Methods and apparatus are provided for allocating a resource to enqueued requests using a predicted wait time that is based on a prior contribution of the resource to service the requests in a particular queue. A resource is allocated to one of a plurality of requests. Each request is stored in at least one of a plurality of queues, each having a predicted wait time. Once it is determined that the resource has become available, the predicted wait times of the queues are updated based on a prior contribution of the resource to the queues; a performance level of each of the queues relative to one or more service level targets; and the resource is assigned, in response to the determination, to the request based on the evaluation. The service level targets can include one or more thresholds for the predicted wait time. The predicted wait time is based on a prior contribution of the resource to servicing one or more of the queues.