Machine Learning Root Cause Analysis for Process Inefficiencies
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Solution Overview
Problem
Current methods for detecting root causes of inefficiencies in organizational processes are time-consuming, expensive, and prone to human error, making them inefficient and costly.
Innovation Solution
A system utilizing machine learning (ML) models to automatically analyze log data and identify root causes of inefficiencies in processes, providing quick, accurate, and cost-effective solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methodology with human consultants is used to detect root cause, then analysis can be performed with human judgment, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces the manual mechanical analysis process performed by human consultants with an automated machine learning system. The ML model processes process optimization data to automatically detect root causes, eliminating the time-consuming manual review while maintaining or improving detection accuracy through algorithmic pattern recognition.
Solution Approach 2:
The system enables self-service root cause analysis by automatically processing data and generating insights without requiring human consultant intervention. The machine learning model independently analyzes process data, identifies inefficiencies, and determines root causes, making the organization self-sufficient in its diagnostic capabilities.
2Measurement precision
If manual methodology with human consultants is used to detect root cause, then human expertise can be applied, but the cost increases significantly
Solution Approach 1:
The patent replaces expensive human consultant services with an automated machine learning system. The ML model, once trained, can perform root cause analysis at minimal marginal cost, eliminating the high fees associated with human expertise while maintaining detection quality through automated pattern recognition and data analysis.
Solution Approach 2:
The system changes the operational parameters from human-based analysis to automated computational analysis. This parameter change transforms the cost structure from high fixed costs (human consultant fees) to lower variable costs (computational resources), making the process more cost-effective while scaling efficiently.
3Productivity
If automated ML model is used to detect root cause, then speed and cost-effectiveness improve, but system complexity increases
Solution Approach 1:
The patent creates a universal machine learning system that handles multiple process types and root cause scenarios through a single platform. The ML model is designed to be multi-functional, capable of analyzing various process optimization data formats and identifying different types of root causes, thereby managing complexity through consolidation rather than proliferation of specialized systems.
Data Source
AI summary
A system for root cause analysis based on process optimization data is provided. The system receives log data associated with a first trace between a first activity and a second activity of a process. The system further determines a state of inefficiency between the first activity and the second activity based on the received log data. The system further applies a first machine learning (ML) model on the received log data. The system further determines a first label and a first value to be associated with the first trace of the process based on the application of the first ML model. The system further generates presentation data associated with the determined state of inefficiency of the first trace based on the determination of the first label and the first value and further transmits the generated presentation data on a user device.