Dynamic Ticket Assignment Engine for Queue Elimination
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Solution Overview
Problem
Conventional trouble ticket work assignment queue management systems are inefficient due to reliance on static skill data, lack of adaptation to dynamic metrics, and manual intervention, leading to inaccurate assignments and increased resource consumption during triage processes, which can delay resolution and introduce human error.
Innovation Solution
A method and system that maintain an ordered list of ticket assignment rules and metrics, dynamically assign tickets based on real-time agent capabilities and past performance metrics, and continuously revise these metrics for improved assignment accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional static skill data and simple assignment rules are used, then system complexity is reduced, but assignment accuracy deteriorates
Solution Approach 1:
The system transitions from static skill data to dynamic real-time metrics that continuously update based on agent performance, availability, and workload. This allows the assignment system to adapt to changing conditions while maintaining manageable complexity through automated metric collection and processing.
Solution Approach 2:
The system implements feedback loops where assignment results are measured and fed back into the metric database. This continuous feedback mechanism improves assignment accuracy over time by learning from past performance data without requiring complex manual intervention.
2Adaptability or versatility
If manual triage processes are used to manage overloaded work, then flexibility in handling complex situations is improved, but time consumption and resource usage increase
Solution Approach 1:
The system enables self-service automation where the assignment engine autonomously manages workload distribution, prioritization, and reassignment based on real-time metrics. This eliminates the need for manual triage while maintaining adaptability through automated decision-making based on learned patterns and current system state.
Solution Approach 2:
The system dynamically adjusts assignment parameters such as priority weights, skill matching thresholds, and workload capacity limits based on real-time conditions. This allows flexible response to changing situations without requiring manual intervention, reducing both time consumption and resource usage.
3Ease of operation
If agents manually scan through all assigned tickets to determine work order, then control over individual decision-making is maintained, but productivity and focus deteriorate
Solution Approach 1:
The system performs preliminary ordering of tickets in the queue based on priority, agent skills, and workload metrics before presentation to the agent. This pre-processing eliminates the need for agents to manually scan and sort tickets, allowing them to immediately begin work on the most appropriate tickets while maintaining organizational objectives.
Data Source
AI summary
Described is a method for work assignment queue elimination. The method includes maintaining at least one data structure including an ordered list of ticket assignment rules and assignment result metrics, receiving a ticket indicating a system problem, assigning the ticket based on the ordered list of ticket assignment rules, the system problem and the assignment result metrics, collecting metrics based on the ticket assignment, and revising the assignment result metrics based on the collected metrics.