IT Service Alert Assignment Algorithm
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Solution Overview
Problem
Random assignment of IT service errors to analysts is inefficient, as it does not consider the specific skills or workload capacities of analysts, leading to potential delays and mistakes in error processing.
Innovation Solution
A computer system that receives, classifies, and assigns IT service error alerts based on severity and analyst skills, monitoring the resolution progress to ensure timely and appropriate handling according to service level agreements (SLAs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If errors are randomly assigned to analysts from a queue, then the assignment process is simple and quick, but the efficiency of error processing is reduced due to mismatch between analyst skills and error types
Solution Approach 1:
The patent replaces the manual coordinator-based assignment system with an automated computer-based system that uses algorithms to match errors with analysts. The system automatically classifies errors, evaluates analyst skills and workloads, and assigns errors optimally without human intervention, thereby improving efficiency while managing complexity through automation.
Solution Approach 2:
The system changes the assignment parameters from random selection to multi-criteria evaluation including error classification (criticality, type), analyst skills, and workload capacity. By incorporating these dynamic parameters, the system optimizes error processing efficiency while maintaining manageable complexity through structured evaluation frameworks.
2Reliability
If a help service coordinator manually classifies and assigns errors, then analyst skills and workload can be considered, but the process is time-consuming and prone to mistakes
Solution Approach 1:
The patent replaces the human coordinator's manual classification and assignment process with an automated computer-based system. The system uses algorithms to classify errors and assign them to analysts, eliminating human errors and reducing assignment time while maintaining or improving accuracy through consistent application of classification criteria.
Solution Approach 2:
The system enables self-service automation where the computer automatically performs error classification and assignment without requiring human coordinator intervention. The automated system serves itself by processing errors through predefined algorithms, reducing both time loss and human error while maintaining high assignment accuracy.
3Productivity
If errors are placed in a general queue for random assignment, then the system is simple to operate, but delays occur due to inability to prioritize critical errors
Solution Approach 1:
The system changes from simple random assignment to a prioritized assignment model that evaluates multiple parameters including error criticality, type, and analyst availability. By incorporating these parameters, the system accelerates resolution of critical errors while maintaining ease of operation through automated evaluation and ranking of errors by priority.
Solution Approach 2:
The system performs preliminary classification and prioritization of errors before assignment to analysts. By pre-evaluating error criticality and matching it with analyst skills and workload, the system ensures that critical errors are assigned first to appropriate analysts, improving resolution speed while keeping the overall process simple through automated preliminary processing.
Data Source
AI summary
In a method for intelligently monitoring and dispatching an Information Technology (IT) service alert, a computer receives a service error alert and classifies the service error alert. The computer assigns the service error alert, based on the service error alert class. The computer monitors the progress of the resolution of the service error alert.