SLA-aware task dispatching with skill development control
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Solution Overview
Problem
Traditional human dispatcher-based ticket dispatching loses valuable knowledge due to high technician rotations, and automated systems degrade job engagement by focusing solely on task load and skill matching, leading to SLA violations and high rotation rates.
Innovation Solution
A skill-development-aware task assignment advisor using unsupervised and supervised machine learning to classify skills, predict resolution times, and control task resolution errors and delays, ensuring appropriate skill development while maintaining SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated dispatching systems focus solely on task load and skill matching, then task allocation efficiency is improved, but technician job engagement deteriorates
Solution Approach 1:
The system changes the parameters of automated dispatching by incorporating skill development potential and engagement metrics alongside traditional skill matching and task load considerations. This multi-parameter approach allows the system to optimize for both efficiency and engagement simultaneously.
Solution Approach 2:
The system implements feedback loops that monitor technician engagement levels and skill development progress, using this information to continuously refine task assignment decisions. This creates a self-improving system that adapts to maintain both productivity and engagement.
2Ease of operation
If human dispatchers manually match technicians to tasks, then job engagement is maintained, but knowledge loss occurs due to high technician rotations
Solution Approach 1:
The system creates a digital copy of dispatcher knowledge by training machine learning models on historical dispatching data, technician skills, and task requirements. This encoded knowledge persists in the system even when individual dispatchers or technicians leave.
Solution Approach 2:
The system transforms qualitative dispatcher knowledge into quantitative parameters that can be processed by algorithms, including skill matching scores, task load metrics, and engagement predictions, making the knowledge machine-processable and retainable.
3Manufacturing precision
If automated systems use traditional skill matching, then task completion accuracy is improved, but SLA compliance deteriorates due to lack of skill development awareness
Solution Approach 1:
The system performs preliminary assessment of both current skills and potential skill development trajectories before task assignment. By evaluating future skill readiness, the system can make assignments that balance immediate task completion quality with long-term SLA compliance.
Solution Approach 2:
The system introduces new parameters for skill development awareness and engagement prediction to the traditional skill matching parameters, creating a more comprehensive evaluation framework that simultaneously optimizes for task accuracy and SLA compliance.
Data Source
AI summary
A processor may receive a new ticket from a ticket management system. The processor may classify, in response to receiving the new ticket, one or more metrics to complete the new ticket. The processor may generate a ticket-metric classification that includes a list of users. The processor may identify a question contained in a digital record of the new ticket. The processor may cluster one or more other tickets into metric levels based on information about the question contained in the digital record of the new ticket. The processor may train a metric model using a database of tickets comprising features extracted from information from the ticket management system. The processor may assign the new ticket to a specific user on the list of users.