Capacity Management Index for Infrastructure Demand Forecasting
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Solution Overview
Problem
Current capacity management in technology infrastructure is inaccurate due to insufficient understanding of business volume relationships with actual demand, leading to unpredictable matching of demand with infrastructure capacity, resulting in excessive or insufficient capacity, and increased costs.
Innovation Solution
A Capacity Management Index (CMI) system that provides predictive and actionable intelligence by measuring the health of technology infrastructure, correlating internal business forecasts with external indicators, and using historical data to derive confidence factors for weighted forecasting, enabling precise capacity planning across various services and components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional business volume forecasting methods are used, then forecasting simplicity is maintained, but forecast accuracy deteriorates
Solution Approach 1:
The forecasting system is segmented into multiple independent components: external indicator analysis, internal business forecast collection, confidence factor calculation, and weighted combination. Each component operates independently and contributes to the overall forecast, allowing for improved accuracy without overwhelming complexity in any single area.
Solution Approach 2:
The patent introduces confidence factors as an intermediary element that mediates between internal business forecasts and external indicators. These confidence factors weigh the reliability of different forecast sources based on historical performance, enabling the system to balance simplicity and accuracy dynamically without requiring complex real-time adjustments.
2Reliability
If capacity buffer is increased to minimize capacity risk, then reliability of capacity supply is improved, but cost increases
Solution Approach 1:
The capacity management system transitions from static capacity buffering to dynamic capacity planning. By continuously updating forecasts using the multi-source forecasting system and calculating accurate demand predictions with confidence factors, the system dynamically adjusts capacity requirements, maintaining reliability while reducing the need for excessive static buffers.
Solution Approach 2:
The system implements feedback loops where historical forecast accuracy is continuously measured and used to update confidence factors. This feedback mechanism allows the system to learn from past performance and improve future predictions, enabling more precise capacity planning that maintains reliability without requiring oversized capacity buffers.
3Measurement precision
If straight line trending is used for demand prediction, then prediction simplicity is maintained, but demand matching precision deteriorates
Solution Approach 1:
The system performs preliminary analysis of external indicators and historical data patterns before generating demand predictions. By pre-processing and correlating multiple data sources in advance, the system builds a more accurate prediction model that goes beyond simple straight-line trending, while organizing the complexity into manageable pre-computed components.
Data Source
AI summary
A system and method is provided that generates an index, providing predictive and actionable intelligence to ensure that a Technology Infrastructure Group (TIG) makes tactical and strategic decisions in support of needs of customers. The invention measures overall health of an infrastructure, specifically with respect to how efficiently various services meet demand placed upon capacity by various Lines of Business. The invention provides an overall assessment of infrastructure capacity management and drills down to various services, components, subcomponents, etc. The invention provides a forecasting tool in which estimates made are not based only upon business forecasts provided by each Line of Business, but also upon forecasts developed by correlating external indicators with historical business volumes. Historical forecasts are compared to actual values to derive a confidence factor used for weighting future Line of Business forecasts. The combined, weighted forecasts obtained are more robust than any individual forecasts on their own.