IT Change Request Success Prediction System
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Solution Overview
Problem
Current change management processes in organizations rely heavily on human prediction, which is prone to error and variability, leading to failed changes that can negatively impact IT infrastructure and customer satisfaction, resulting in resource wastage and reputational damage.
Innovation Solution
A computer-implemented system using machine learning algorithms and prediction tools to analyze parameters of change requests based on historical data, providing accurate probability of success or failure predictions, and offering recommendations to improve success rates, thereby reducing human error and focusing efforts on high-risk changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human prediction is used to assess change requests, then subjective judgment and experience can be applied, but error and variability increase leading to failed changes
Solution Approach 1:
The patent replaces the mechanical system of human judgment with an automated machine learning model that processes change request parameters objectively. The model uses historical data and predefined parameters (scope, risk, resources, timeline) to generate consistent success probability predictions, eliminating human subjectivity and variability while maintaining reliability through data-driven analysis.
Solution Approach 2:
The system enables self-service prediction where the machine learning model automatically assesses change requests without requiring human intervention for each prediction. The model uses stored historical data and parameters to generate predictions independently, improving consistency while maintaining high reliability through automated decision-making based on learned patterns.
2Measurement precision
If automated prediction tools are implemented, then consistency and objectivity improve, but system complexity increases
Solution Approach 1:
The patent segments the prediction system into distinct modular components: parameter extraction modules that identify specific attributes from change requests, a machine learning model that processes these parameters, and an output module that generates success probability predictions. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining consistent predictions through standardized processing.
Solution Approach 2:
The system manages complexity by focusing on a defined set of key parameters (scope, risk, resources, timeline) rather than analyzing all possible change request attributes. By transforming complex unstructured data into standardized parameter inputs, the system achieves consistent predictions with controlled complexity through parameter normalization and selection.
3Reliability
If comprehensive historical data is analyzed, then prediction accuracy improves, but data processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing historical change request data in structured formats before actual predictions are needed. The machine learning model is trained in advance on comprehensive historical datasets, and parameter extraction rules are pre-defined. This allows the system to quickly retrieve and process only relevant parameters during actual predictions, maintaining high accuracy while reducing real-time processing time.
Data Source
AI summary
The present disclosure involves systems, software, and computer-implemented methods for automatically predicting a probability of success or failure of change requests for an organization's information technology (IT) infrastructure or use environment. One example system includes at least one repository storing information corresponding to one or more prior change requests, at least one memory storing instructions and at least one prediction tool, and at least one hardware processor. Each change request corresponds to a request for a modification to one or more sections of the infrastructure. The instructions instruct the at least one hardware processor to receive one or more parameters corresponding to a new change request associated with the infrastructure. The parameters are provided as input to the at least one prediction tool, and in response, a probability of success of the new change request is received as an output of the at least one prediction tool.