ML Skill Prediction for Incident Resolution Time
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Solution Overview
Problem
Incident management systems face challenges in efficiently assigning personnel with the right skills to resolve IT incident tickets within the required resolution time, leading to potential service level agreement (SLA) breaches and suboptimal resource allocation.
Innovation Solution
A method and system utilizing machine learning to analyze historical incident reports, identify necessary skills for timely resolution, and predict the required skills for new incident tickets, thereby optimizing personnel assignment based on predicted skill sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional incident management systems manually assign personnel to incident tickets, then personnel can be assigned based on experience and judgment, but the resolution time may exceed the required threshold and resource allocation becomes suboptimal
Solution Approach 1:
The system enables self-service through automated skill-based assignment. The machine learning model automatically analyzes incident tickets, identifies required skills, and assigns personnel without manual intervention. This self-service mechanism resolves the contradiction by eliminating manual assignment delays while maintaining optimal resource allocation based on skill matching.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on historical incident data before actual incident assignment. The model learns from past patterns of skill requirements and resolution outcomes, enabling it to make accurate predictions about optimal personnel assignment. This preliminary training phase ensures that when incidents occur, assignments are made quickly and efficiently based on proven patterns.
2Measurement precision
If incident management systems increase automation in personnel assignment, then resolution time prediction accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system replaces manual mechanical assignment processes with an automated machine learning-based system. Instead of human managers manually reviewing and assigning personnel, the ML model automatically predicts required skills and makes assignments. This substitution increases prediction accuracy while the modular implementation keeps complexity manageable through standardized interfaces and pre-trained models.
Solution Approach 2:
The machine learning model acts as an intermediary between incident tickets and personnel assignment. It receives incident data, processes it through learned patterns, and outputs predicted skill requirements and assignments. This intermediary layer simplifies the overall system architecture by centralizing the decision-making logic in a single trainable component rather than distributing complex rules across multiple systems.
3Reliability
If machine learning models are trained on historical incident data to predict skill requirements, then personnel assignment accuracy improves, but the training process and model maintenance increase system complexity
Solution Approach 1:
The system leverages parameter changes in the machine learning model through continuous training on new incident data. As the model processes more incidents, it updates its internal parameters to reflect changing patterns in skill requirements and resolution outcomes. This dynamic parameter adjustment maintains high assignment accuracy while the underlying complexity is managed through automated retraining pipelines and standardized model versions.
Data Source
AI summary
A system to provide end users with recommendations on improving the quality of the incident management process is provided. A computer device identifies a set of historical incident reports, wherein the historical incident reports identify: (i) incident tickets, (ii) one or more skills associated with personnel assigned to the incident tickets, and (iii) whether the incident tickets were resolved within threshold periods of time to resolve. The computing device trains a machine learning model to predict sets of skills associated with resolving incident tickets within the threshold periods of time to resolve based, at least in part, on the identified set of historical incident reports. The computing device assigns a set of personnel to the new incident ticket based, at least in part, on the predicted set of skills associated with resolving the new incident ticket within the threshold period of time to resolve.