Configurable Exception Rules for Manufacturing Quality Review
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Solution Overview
Problem
Quality review in manufacturing processes is labor-intensive and inefficient due to the need for quality engineers to manually sift through vast amounts of data in batch log files to identify exceptions, which are often buried within non-relevant information, requiring significant time and expertise to determine the context and severity of deviations from quality standards.
Innovation Solution
A quality review management system that automatically detects exceptions using configurable rules and an exception engine, storing data in an organized manner and providing a user-friendly interface for quality engineers to review and handle exceptions, enabling real-time feedback and integration with third-party systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quality engineers manually review batch log files to identify exceptions, then they can detect quality deviations, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated computer-based system that retrieves batch data, applies quality rules, and generates exception reports automatically. This substitution eliminates manual labor while maintaining exception detection capability through systematic automated analysis of batch logs against predefined quality standards.
Solution Approach 2:
The system enables self-service quality review by automatically performing data retrieval, analysis, and exception identification without requiring quality engineers to manually examine batch logs. The automated generation of exception reports with contextual information allows the system to serve its own quality review function independently.
2Reliability
If quality engineers manually analyze vast amounts of batch log data, then they can identify exceptions, but the complexity of data retrieval and analysis increases
Solution Approach 1:
The patent segments the complex quality review process into distinct automated components: data retrieval from batch logs, rule-based exception analysis, contextual information gathering, and report generation. This segmentation simplifies the overall complexity by breaking down the monolithic manual review task into manageable automated steps that can be executed systematically.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the raw batch data and the quality engineer. This intermediary automatically retrieves data, applies quality rules, and presents processed exception information, thereby reducing the complexity of direct data analysis while ensuring reliable quality standard compliance through systematic rule application.
3Reliability
If quality engineers review all batch data to ensure quality standards, then comprehensive quality assurance is achieved, but productivity decreases due to manual effort
Solution Approach 1:
The patent replaces manual quality review mechanics with automated computer-based processing that can analyze batch data at much higher speeds. This substitution maintains comprehensive quality assurance through systematic rule application while increasing productivity by eliminating the time constraints of manual review and enabling parallel processing of multiple batches.
Solution Approach 2:
The system enables continuous automated quality review operations without the interruptions inherent in manual processes. The automated system can continuously retrieve batch data, apply quality rules, and generate exception reports without breaks, thereby maintaining comprehensive quality assurance while significantly increasing overall review throughput and productivity.
4Measurement precision
If quality engineers need extensive knowledge of multiple data systems to review batch processes, then accurate exception identification is possible, but the ease of operation decreases
Solution Approach 1:
The system creates a simplified copy or representation of the complex batch data through automated exception reports that present only relevant quality deviations with contextual information. This copying approach maintains accurate exception identification by systematically analyzing all underlying data while presenting results in an easily accessible format that does not require deep knowledge of the source data systems.
Solution Approach 2:
The automated system acts as an intermediary that handles the complexity of interfacing with multiple data systems, applying quality rules, and retrieving contextual information. This intermediary shields quality engineers from system complexity while maintaining accurate exception identification through systematic automated analysis, thereby improving ease of operation without sacrificing detection accuracy.
Data Source
AI summary
A quality review management system may be used to analyze the operation of manufacturing processes within a plant based on data collected by various data sources in the plant, such as batch executive applications, to automatically detect, store, and display exceptions within those processes for use by a quality review engineer to determine if the process operation meets certain quality standards. The quality review management system includes a configuration application that enables a user to create one or more exception rules, an exception engine that analyses process data using the rules to detect one or more exceptions within the process, and a review application that enables quality review personnel to review each determined exception for resolution purposes.