Queue Look Ahead Work Assignment Engine
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Solution Overview
Problem
Existing work allocation methods in customer engagement centers lead to inefficiencies due to agents being mismatched with work items based on skill sets, resulting in increased wait times, reallocation, and customer dissatisfaction.
Innovation Solution
A system and method that analyzes work queue and agent skills in advance to optimize work item assignment by matching agents with the most qualified available agents, minimizing misallocation and maximizing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the next available agent method is used for work allocation, then work items are assigned quickly to available agents, but agents may be mismatched with work items based on skill sets, leading to longer completion times and reallocation needs
Solution Approach 1:
The system performs preliminary analysis of the work queue to identify imminent work items and their required skills before making allocation decisions. By looking ahead at multiple work items in the queue and pre-assessing skill requirements, the system can make more informed allocation decisions that match agents with appropriate skills while considering future work demands, thereby reducing reallocation needs and improving overall productivity
Solution Approach 2:
The system continuously monitors agent availability, skill sets, and work queue composition, using this feedback to dynamically optimize work allocation. By analyzing the composition of the work queue and agent capabilities in real-time, the system adjusts allocations to balance speed of assignment with appropriate skill matching, reducing both completion time and reallocation frequency
2Reliability
If specialized agents are assigned to complex work items, then work items are handled with appropriate expertise, but specialized agents may be unavailable for other specialized work items, reducing overall system efficiency
Solution Approach 1:
The system performs preliminary analysis of the work queue to identify imminent work items and their required skills before making allocation decisions. By looking ahead at multiple work items in the queue and pre-assessing skill requirements, the system can strategically allocate specialized agents to the most appropriate work items while preserving availability for future specialized demands, thereby maintaining high handling quality while improving overall agent utilization efficiency
Solution Approach 2:
The system dynamically adjusts work allocation based on real-time conditions including agent availability, skill sets, and work queue composition. By continuously adapting allocations rather than using static assignment rules, the system can flexibly assign specialized agents to appropriate work items while maintaining the ability to respond to changing demands, thus balancing reliability with productivity
3Ease of operation
If work items are queued in chronological order based on arrival time, then first-come-first-served fairness is maintained, but agents may be assigned to work items without appropriate skills, requiring reallocation
Solution Approach 1:
The system performs preliminary analysis of the work queue to identify imminent work items and their required skills before making allocation decisions. By pre-assessing skill requirements for multiple upcoming work items and comparing them with available agent skills, the system can make informed allocations that reduce reallocation needs while maintaining operational simplicity through automated decision-making
Solution Approach 2:
The system introduces an intermediary optimization layer between the chronological work queue and agent assignment. This intermediary analysis layer evaluates both queue position and skill requirements, acting as a mediator that balances first-come-first-served fairness with appropriate skill matching, thereby reducing reallocation time while preserving queue management simplicity
Data Source
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AI summary
In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing agents being assigned to work items they are most qualified to handle. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize the work item being assigned the best qualified agent. The work items are then routed to the agents accordingly.In the present disclosure, analytics are applied to work items while the work items are waiting in a work queue in order to optimize the routing and allocation of work items to agents in the most efficient manner possible, while optimizing agents being assigned to work items they are most qualified to handle. By performing a look ahead at more than the initial work item, the system assesses the agent skills required by imminent work items in the work queue. This is then compared to a skillset of each available and/or soon to be available agent in order to achieve the optimal allocation of the work items to maximize the work item being assigned the best qualified agent. The work items are then routed to the agents accordingly.