Automated Supply Chain Data Analysis for Root Cause Detection
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Solution Overview
Problem
Current methods for analyzing manufacturing, test, return, and supply chain data are time-consuming and inefficient, often relying on rudimentary tools that fail to provide deep analytics or identify root causes effectively, leading to suboptimal decision-making in manufacturing processes.
Innovation Solution
A system and method for supply chain data analysis that stores and processes data from multiple sources, using databases and multi-dimensional failure analysis to detect faulty combinations of factors, perform correlation analysis, and generate insights for improving manufacturing efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automatic analysis using rudimentary software is used, then users can perform basic data analysis, but the process is time-consuming and does not guarantee finding problems or suggesting solutions in reasonable time
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated computer-based systems. The system automatically performs data extraction, combination generation, fault detection, and root cause analysis without manual intervention, substituting the mechanical process of manual analysis with an automated computational system that delivers both high accuracy and fast results
Solution Approach 2:
The system performs self-service by automatically executing the complete analysis workflow including data extraction, combination generation, fault detection, and root cause identification without requiring manual operation. The system serves itself by autonomously processing data and generating insights, eliminating the time-consuming manual analysis process while maintaining high accuracy
2Adaptability or versatility
If off-the-shelf Business Intelligence tools are used, then general data analysis features are available, but directed questions specific to a data domain become impossible or require sophisticated steps making the process cumbersome
Solution Approach 1:
The patent changes the parameters of the analysis system by implementing domain-specific analysis modes that automatically adjust the analysis methodology based on the type of data being analyzed. The system can switch between different analysis paradigms (fault detection, root cause analysis, trend analysis) and automatically configures the appropriate parameters for each domain, providing both flexibility and simplicity
Solution Approach 2:
The patent introduces an intermediary layer between the user and the complex data analysis processes. This intermediary system automatically translates user-friendly queries into sophisticated analysis operations, handling the complexity internally while presenting simple interfaces to users. The intermediary manages the complex steps behind the scenes without requiring users to understand or execute them manually
3Ease of operation
If users are limited to reporting tools, then the process is simple to operate, but users tend to ignore the value of data insights
Solution Approach 1:
The patent segments the data analysis output into multiple levels of insight, from basic reporting to advanced analytical findings. The system divides the comprehensive analysis results into organized categories including fault detections, root causes, trends, and actionable insights, making the valuable information accessible while maintaining ease of operation through structured presentation
Solution Approach 2:
The patent adds another dimension to data presentation by transforming flat reporting into multi-dimensional analytical insights. The system enhances basic reports with additional dimensions such as causal relationships, predictive trends, and contextual information, allowing users to access deep insights without sacrificing operational simplicity through intuitive interfaces
Data Source
AI summary
A method and system for supply chain data analysis. The method includes storing supply chain data including test data, genealogy data, repair data, some factors and some items, in one or more databases and selecting a portion of the factors from the stored data, and a time range for analysis. The method then selects one or more criterion for analysis; extracts a portion of the stored data; analyzes the extracted portion of the stored data to detect a plurality of faulty combination of factors and items that results in an unexpected change in a key performance index, according to said extracted portion of the plurality of combinations. The method then performs correlation analysis on said plurality of faulty combinations to determine a root cause for the detected combination of factors; and generates a subset of said plurality of faulty combinations, according to said root causes of said plurality of faulty combinations.