Predictive Product Quality Assessment via Readiness Model
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Solution Overview
Problem
Characterizing product readiness based on data from disparate sources, such as manufacturing facilities and sensors, is challenging, leading to unwarranted testing and remedial actions, which are expensive and time-consuming, and issues may persist after delivery.
Innovation Solution
A method using initialization product assessment data to initialize a product readiness model, which determines a product readiness score based on run-time product assessment data, incorporating remediation time, reassessment status, and recurring event status to consolidate information from various data sources into a single score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If product assessments are performed using data from disparate sources (manufacturing facilities, sensors, testing equipment), then product quality characterization is improved, but the complexity of data integration and assessment increases
Solution Approach 1:
The patent segments the complex assessment system into distinct modules: data collection module that gathers data from disparate sources (manufacturing facilities, sensors, testing equipment), data processing module that normalizes and integrates the segmented data, and assessment module that generates quality scores. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining comprehensive product quality characterization.
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between raw data from disparate sources and the final quality assessment. This intermediary layer standardizes data formats, handles data normalization, and integrates information from multiple sources before passing it to the assessment module, thereby simplifying the integration of heterogeneous data sources.
2Reliability
If traditional testing and remedial actions are performed without predictive analysis, then product issues can be identified, but time and resources are wasted on unwarranted testing
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and analyzing product data during manufacturing and testing phases to generate predictive quality scores before final product delivery. This allows potential issues to be identified and addressed proactively during the manufacturing process, preventing defective products from reaching the customer and eliminating the need for time-consuming post-delivery troubleshooting and recalls.
Solution Approach 2:
The patent implements feedback mechanisms where quality assessment results are fed back into the manufacturing process in real-time. When the predictive quality score indicates potential issues, the system triggers alerts and recommends remedial actions, allowing operators to adjust manufacturing parameters or address problems immediately. This continuous feedback loop reduces the need for extensive traditional testing by enabling early detection and correction of issues.
3Measurement precision
If comprehensive product assessment data is collected and analyzed, then product readiness can be accurately determined, but the time and computational resources required increase
Solution Approach 1:
The patent extracts and focuses on the most critical quality indicators and key performance parameters from the comprehensive product assessment data, rather than analyzing all available data equally. By identifying and prioritizing the most influential factors affecting product readiness, the system achieves accurate assessment results while reducing computational complexity and processing time, effectively taking out only the essential elements needed for reliable quality determination.
Data Source
AI summary
A method for predictive product quality assessment comprises receiving initialization product assessment data for a plurality of products. The initialization product assessment data comprises, for each product, one or more events, and for each of the one or more events, an amount of remediation time, a reassessment status, and a recurring event status. The initialization product assessment data is used to initialize a product readiness model to determine a product readiness score based upon run-time product assessment data. The run-time product assessment data comprises, for a selected product, one or more run-time events. For each of the one or more run-time events, a run-time reassessment status and a run-time recurring event status are obtained. A total run-time remediation time, the run-time reassessment status and the run-time recurring event status are input into the product readiness model. The product readiness model is utilized to determine and output the product readiness score.