Process Stream Grading Engine for Manufacturing Quality Analysis
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Solution Overview
Problem
Conventional quality measurement systems, relying on Cpk and Ppk ratios, face limitations such as limited understanding, lack of visibility into corrective actions, and inadequacy as enterprise-level comparative metrics, necessitating a more automated and dashboard-driven approach for manufacturing quality analysis.
Innovation Solution
A cloud-based quality analysis engine that automatically grades and displays manufacturing process streams using a graphical user interface, providing intuitive dashboards for quick analysis and decision-making, incorporating metrics like yield potential and performance grades to identify deficiencies and highlight best practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Cpk and Ppk ratios are used for quality measurement, then process performance can be quantified, but the system becomes difficult to understand and lacks actionable insights for corrective actions
Solution Approach 1:
The patent transforms conventional quality metrics (Cpk, Ppk) into a new grading system with letter grades (A-F) and numeric scores (0-100). This parameter transformation makes the data more interpretable for diverse audiences while maintaining the underlying statistical rigor. The grading system converts complex ratio-based metrics into intuitive categorical and numerical representations that are easier to understand and act upon.
Solution Approach 2:
The patent introduces an intermediary layer between raw quality data and decision-making. The grading engine acts as a mediator that processes complex statistical calculations and translates them into actionable grades and recommendations. This intermediary layer simplifies the information flow, making quality data more accessible to executives and operators without sacrificing measurement precision.
2Measurement precision
If single-stream Cpk and Ppk ratios are used, then specific process performance can be analyzed, but enterprise-level comparative metrics are not provided
Solution Approach 1:
The patent creates a universal grading framework that can evaluate multiple process streams across different manufacturing operations. The same letter grade and numeric score system applies consistently across all streams, enabling direct comparison and ranking. This multi-functional system serves both detailed process analysis and enterprise-wide quality benchmarking simultaneously.
Solution Approach 2:
The patent adds new dimensions to quality measurement by introducing letter grades (A-F) and numeric scores (0-100) alongside traditional Cpk/Ppk ratios. This dimensional expansion allows for both detailed process-level analysis and high-level enterprise comparisons. The grading system organizes complex multi-stream data into hierarchical structures that facilitate comparative analysis across the entire organization.
3Reliability
If detailed quality data is collected and analyzed, then process deficiencies can be identified, but the system complexity increases
Solution Approach 1:
The patent extracts the essential information from complex quality datasets and presents it through simplified grading indicators. The grading engine isolates key performance characteristics and represents them through letter grades and numeric scores, removing unnecessary statistical complexity while maintaining reliability. This extraction process makes the system more manageable and easier to implement without sacrificing quality verification accuracy.
Data Source
AI summary
A cloud-based process stream analysis and grading engine for manufacturing and business applications. As a module contained within a comprehensive manufacturing quality suite, the grading engine functions are divided into two stages. For each unique part/process/feature data stream, Stage one automatically generates and stores a daily statistical summary record. These records are summarized from millions of raw data values fed into the engine across thousands of process streams. Stage two, the grading function, compares a user-specified time bounded analysis of each stream's summary history to the respective engineering specifications, resulting in a concatenated dual character grade—a letter and a number. There are a total of nine possible grade outcomes (A1, A2, A3, B1, B2, B3, C1, C2, and C3). The ABC portion of the grade ranks the Yield Potential. The 123 portion ranks the Yield Performance. The grades are reported to the users on the Grading Matrix tile and/or Site Summary tile contained within a built-in dashboard.