ML Service Provider Performance Evaluation
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Solution Overview
Problem
Current service provider performance evaluation methods are inefficient and inaccurate due to manual interventions, leading to increased costs, errors, and inconsistencies, which hinder effective decision-making in identifying high-quality IT partners.
Innovation Solution
The implementation of machine learning models to analyze service ticket data, calculate performance metrics, and generate scores for service providers, allowing for the evaluation of overall performance based on multiple criteria, including user feedback and service delivery metrics, while accurately attributing scores to all involved service providers in a service ticket's lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual service provider performance evaluation is used, then evaluation can be performed with existing survey methods, but it leads to increased manual inputs, costs, inefficiencies, errors, and inconsistencies
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with automated machine learning models. The system extracts data from service tickets, aggregates records, calculates performance metrics, and generates evaluations automatically using ML algorithms, eliminating the need for manual survey completion and analysis while significantly improving evaluation efficiency and reducing time loss
Solution Approach 2:
The system enables self-service evaluation by automatically processing service ticket data, extracting relevant information, and generating performance evaluations without requiring manual intervention. The machine learning model autonomously performs the evaluation function, allowing the system to serve itself rather than requiring human analysts to manually assess performance
2Measurement precision
If manual service provider performance evaluation is used, then evaluation can be conducted with existing methods, but it results in increased errors and inconsistencies
Solution Approach 1:
The patent replaces manual evaluation with machine learning-based automated evaluation. The system processes service ticket data through structured extraction, aggregation, and calculation algorithms that consistently apply the same criteria, eliminating human error and ensuring reliable, consistent evaluation results across all service providers
Solution Approach 2:
The system incorporates feedback mechanisms where performance metrics are calculated based on actual service ticket data, and evaluations are continuously updated. This feedback loop ensures that the evaluation system learns from actual performance data and maintains high accuracy and consistency in its assessments
3Measurement precision
If traditional service provider evaluation methods are used, then evaluation processes can be maintained as-is, but decision making becomes less accurate
Solution Approach 1:
The patent segments the evaluation process into distinct modular components: data extraction from service tickets, record aggregation by timing, performance metric calculation, and machine learning-based evaluation. This segmentation allows each component to be optimized independently while working together to provide accurate decision-making support
Solution Approach 2:
The system changes the parameters of evaluation by transitioning from manual survey-based metrics to automated machine learning models that process multiple data parameters from service tickets. This parameter transformation enables more accurate and comprehensive evaluation while the modular architecture keeps the system manageable despite the increased complexity
Data Source
AI summary
A method and system for cloud based service provider performance evaluation are disclosed. The method may include extracting service record data and ticket status change record data from the service ticket data and aggregating the service record data and the ticket status change record data. The method may include calculating ticket level performance metric data based on the aggregated record data and generating ticket level performance scores based on the ticket level performance metric data. The method may further include generating service level performance scores based on the service level performance metric data and generating service feedback performance scores based on the service feedback metric data. The method may further include merging the ticket level performance scores, the service level performance scores, and the service feedback performance scores to generate performance vectors and evaluating overall performances of the service providers based on the set of performance vectors.