IT Service Metrics Analysis via Causal Loop Diagrams
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Solution Overview
Problem
Conventional IT production service support metrics tools fail to consider the relationships between metrics from different processes, limiting their ability to analyze the causes of deviations and provide proactive solutions.
Innovation Solution
A method and system that utilize a causal loop diagram to identify deviant metrics, trace associated variables, and perform iterative factor analysis to determine the cogent factors causing deviations, thereby analyzing the inter and intra relationships between metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing tools provide dashboards showing deviant metrics independently, then the monitoring of individual metrics is simplified, but the analysis of relationships between metrics from different processes is lost
Solution Approach 1:
The patent merges multiple independent metric monitoring functions into a unified system that simultaneously tracks individual metrics and their inter-process relationships. The causal loop diagram integrates metrics from different processes (incident management, problem management, change management) into a single visual representation, allowing users to monitor both individual deviant metrics and their contextual relationships without switching between separate dashboards.
Solution Approach 2:
The causal loop diagram serves as an intermediary structure that mediates between individual metric data and the relationships between processes. It provides a visual intermediate layer that connects metrics from different processes, enabling users to understand relationships without adding complexity to the individual metric monitoring interface.
2Device complexity
If existing tools treat metrics independently, then the complexity of the system is reduced, but the ability to analyze causes of deviation is limited
Solution Approach 1:
The patent segments the causal loop diagram into distinct process areas (incident management, problem management, change management) while maintaining the overall integrated structure. Each process can be analyzed independently through its specific metrics, yet the segmentation allows for easy traversal of relationships between processes. This segmentation reduces cognitive load while preserving the ability to perform comprehensive cause analysis.
Solution Approach 2:
The patent adds a new dimensional layer to traditional metric monitoring by incorporating causal relationships as a third dimension. Instead of only monitoring metrics in one dimension (value) and process in another (category), the causal loop diagram creates a three-dimensional structure where metrics are connected through causal links, enabling users to analyze causes of deviation by traversing multiple dimensions simultaneously.
3Measurement precision
If iterative factor analysis is performed on multiple metrics, then the identification of cogent factors is improved, but the computational time and processing complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-establishing causal loop diagrams and pre-identifying potential cogent factors before actual deviation analysis is needed. The system maintains updated causal relationship maps and metric baselines, so when deviations occur, the iterative factor analysis can proceed more quickly using pre-computed structural information rather than analyzing all relationships from scratch.
Solution Approach 2:
The patent applies local quality by focusing the iterative factor analysis only on the local neighborhood of deviant metrics and their directly connected causal relationships, rather than performing comprehensive analysis across the entire system. The causal loop diagram enables the system to identify and analyze only the relevant subset of metrics and relationships needed to explain specific deviations, reducing computational overhead while maintaining analysis precision.
Data Source
AI summary
The disclosure relates generally to information technology (IT) production service support metrics and, more particularly, to analysis of the IT production service support metrics. The method includes receiving a plurality of metrics of an IT service management system and obtaining a causal loop diagram for the IT service management system. Subsequently, determining one or more deviant metrics from the plurality of metrics and obtaining associated CLD variable. Further, determining first set of metrics that affect the deviant metric and second set of metrics that may get affected. Subsequently, iteratively performing factor analysis on first set of metrics and second set of metrics to determine cogent factor for the deviation in the deviant metrics.