ML Capacity Forecasting for Technology Infrastructure Roadmaps
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Solution Overview
Problem
Business enterprises face challenges in anticipating technology infrastructure needs and planning for replacements and upgrades, leading to potential operational disruptions and inefficiencies.
Innovation Solution
A system and method utilizing a product roadmap and machine learning to predict technology infrastructure capacity requirements, automatically validating and provisioning necessary resources to meet these needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and planning is used for technology infrastructure capacity needs, then human judgment and flexibility are maintained, but the process is time-consuming and lacks predictive accuracy
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated machine learning system. The ML model automatically processes product roadmap data, historical infrastructure information, and business metrics to predict future capacity requirements, eliminating the need for manual analysis while improving both speed and accuracy of predictions.
Solution Approach 2:
The system performs self-service by automatically generating infrastructure capacity predictions without requiring continuous human intervention. The machine learning model continuously learns from historical data and automatically updates its predictions, allowing the system to serve itself in the analysis and planning process.
2Reliability
If reactive infrastructure planning is used, then current needs are addressed, but future capacity requirements are not anticipated leading to operational disruptions
Solution Approach 1:
The patent implements preliminary action by using machine learning to forecast future infrastructure capacity requirements before they are needed. The system analyzes product roadmaps and historical data to predict future needs, allowing organizations to proactively plan and provision infrastructure resources in advance, ensuring operational continuity and eliminating reactive planning.
3Manufacturing precision
If detailed manual planning is performed for technology infrastructure, then accuracy of resource allocation is improved, but the complexity and time required increase significantly
Solution Approach 1:
The system replaces complex manual planning processes with an automated machine learning model that handles the complexity of resource allocation calculations. The ML model processes multiple variables including product roadmap data, historical infrastructure usage patterns, and business metrics to generate accurate resource allocation predictions, simplifying the planning process while maintaining or improving accuracy.
Solution Approach 2:
The patent changes the approach from manual parameter analysis to automated machine learning parameter processing. The system transforms qualitative product roadmap information and historical data into quantitative predictions through ML algorithms, automatically adjusting and optimizing resource allocation parameters without manual intervention, thereby reducing complexity while maintaining precision.
Data Source
AI summary
Various examples are directed to computer-implemented systems and methods for predictive analysis of technology infrastructure capacity requirements. A method includes receiving a product roadmap input indicating technological requirements of an enterprise, and analyzing the product roadmap input to locate and extract capacity data from the product roadmap input. Using machine learning, technology infrastructure capacity requirements are predicted for the enterprise based on the capacity data. The technology infrastructure capacity requirements are validated, and technological resources are determined to meet the technology infrastructure capacity requirements. When the technology infrastructure capacity requirements are validated, the technological resources are automatically provisioned to meet the technology infrastructure capacity requirements.