Help Ticket Assignment via Resource Workload Analysis
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Solution Overview
Problem
Current help-desk systems often inefficiently assign call tickets based on arbitrary skillsets and availability, leading to 'cherry picking' of easy issues and neglect of complex problems, without considering the expertise or workload of help-desk resources.
Innovation Solution
A system comprising a help ticket assignment module, request analyzer module, and task load monitor module that analyzes digital help requests for characteristics like topic, urgency, and priority, and evaluates help-desk resources' suitability and availability based on response time, quality, workload, and other factors to assign tickets to the most appropriate resource.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated routing is used to assign tickets to the next available resource, then assignment speed is improved, but assignment quality deteriorates because it ignores resource expertise and performance history
Solution Approach 1:
The system incorporates feedback loops that continuously monitor resource performance metrics, ticket resolution outcomes, and workload distribution. This feedback is used to dynamically adjust assignment decisions, ensuring that automated routing considers both speed and quality factors by learning from historical data and adapting to changing conditions
Solution Approach 2:
The system changes multiple parameters simultaneously including resource expertise level, current workload, performance history, and ticket complexity to optimize both assignment speed and quality. By adjusting these parameters dynamically rather than relying on a single static rule, the system achieves balanced optimization of both contradictory requirements
2Ease of operation
If resources pick their own tickets, then resource autonomy is improved, but work distribution deteriorates due to cherry-picking of easy issues
Solution Approach 1:
The system implements dynamic ticket assignment that adapts to both resource preferences and system needs. Resources can express preferences within defined parameters, but the system dynamically adjusts assignments to ensure balanced workload distribution, preventing cherry-picking while maintaining reasonable autonomy through configurable preference weights
Solution Approach 2:
The automated assignment system acts as an intermediary between resource preferences and ticket requirements. It mediates by considering resource autonomy preferences while simultaneously ensuring equitable work distribution, balancing both competing interests through algorithmic mediation rather than direct resource-ticket pairing
3Device complexity
If arbitrary skillset matching is used for ticket assignment, then assignment complexity is reduced, but resolution quality deteriorates due to lack of consideration for performance history and current workload
Solution Approach 1:
The system segments the assignment decision-making process into distinct modules: skillset matching, performance history evaluation, workload assessment, and priority weighting. This segmentation allows each factor to be processed independently with appropriate complexity, then integrated to produce high-quality assignments without requiring the entire system to be overly complex
Data Source
AI summary
Embodiments of the present invention relate to systems and methods for automating the assignment of digital help-desk requests. An embodiment of the invention includes a request analyzer module which analyzes the content of the digital help-desk request, a task load monitor module which analyzes the help-desk resource availability and suitability, and a help-ticket assignment module with generates a help ticket based on the digital help-desk request and assigns the help ticket to a resource based upon the analysis of the request and the analysis of resource availability and suitability. The system may also estimate a time to respond to the request based upon an analysis of the assigned resources capabilities and status.