Manufacturing Failure Rate Reduction via Formula-Based Product Clustering
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Solution Overview
Problem
Conventional manufacturing defect management systems face challenges in analyzing failure rates due to insufficient data, especially when production volumes are low, making it difficult to identify causes and improve processes effectively.
Innovation Solution
The implementation of a computer-based system that clusters products by their manufacturing formulas to generate larger data sets, allowing for improved analysis of failure rates and optimizing manufacturing processes, formulas, and quality parameters through techniques like TF-IDF scoring, clustering, and predictive modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manufacturing rates for a particular product are low, then production volume is reduced, but there is not enough data to perform proper analysis of failure rates and causes
Solution Approach 1:
The patent merges data from multiple similar products into unified product clusters based on formula similarity. By combining manufacturing and test data across clustered products, the system creates sufficiently large data sets even when individual product volumes are low, enabling statistically valid failure rate analysis while maintaining low production rates for specific products.
2Measurement precision
If products are analyzed individually, then specific product data is maintained, but data sets remain too small for proper failure rate analysis
Solution Approach 1:
The system combines data from multiple products by clustering them according to formula similarity. This merging approach increases the quantity of available data while maintaining analytical precision through similarity-based grouping, allowing reliable failure rate measurements that would be impossible with individual product data alone.
Solution Approach 2:
The patent applies local quality by creating heterogeneous clusters where products are grouped based on their specific formula characteristics. Each cluster maintains local similarity (products with comparable formulations) while achieving global data sufficiency. This allows precise analysis tailored to each product's specific ingredients and manufacturing parameters while benefiting from aggregated data volume.
3Ease of manufacture
If conventional defect management systems are used, then existing processes are maintained, but analysis of manufacturing formulas and processes remains difficult due to data insufficiency
Solution Approach 1:
The system implements feedback by continuously analyzing test results and manufacturing data from clustered products, then using these insights to identify failure causes and recommend process improvements. The feedback loop transforms raw data into actionable information about ingredient-test relationships and process optimizations, making formula and process analysis systematically easier while preventing information loss about failure causes.
Solution Approach 2:
The patent performs preliminary action by pre-processing and organizing manufacturing data into structured formats before analysis. The system pre-establishes product clusters and organizes test results in advance, creating ready-to-analyze data structures that simplify subsequent failure rate analysis and eliminate the need for complex manual data gathering and preparation.
Data Source
AI summary
Systems and methods are provided for reducing failure rates of a manufactured products. Manufactured products may be clustered together according to similarities in their production data. Manufactured product clusters may be analyzed to determine mechanisms for failure rate reduction, including adjustments to test quality parameters, product formulas, and product processes. Recommended product adjustments may be provided.