Rapid Operational Analysis System for Real-Time Defect Identification
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Solution Overview
Problem
Current industrial processes lack efficient methods to quantify and address bottlenecks and process limitations in real-time, leading to suboptimal resource allocation and prolonged defect resolution times.
Innovation Solution
The Rapid Operational Analysis System (ROAS) within the Industrial Business Data Management System (IBDMS) analyzes time-stamped industrial process data to identify deviations, set statistical control parameters, and shift resources to eliminate excesses, providing immediate cost savings estimates and actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time data analysis is performed to identify deviations and set statistical control parameters, then defect identification speed is improved, but system complexity increases
Solution Approach 1:
The system segments the analysis process into distinct modules: data collection, statistical parameter calculation, deviation identification, and resource reallocation. Each module operates independently and processes specific portions of the industrial process data, enabling real-time analysis while maintaining manageable system complexity through functional decomposition.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring industrial process data, comparing it against statistical control parameters, and automatically triggering resource reallocation when deviations are detected. This feedback mechanism enables rapid defect identification while the automated nature of the loop reduces operational complexity.
2Productivity
If resources are shifted to eliminate excesses immediately, then productivity is improved, but resource allocation complexity increases
Solution Approach 1:
The system performs self-service resource allocation by automatically analyzing process data, identifying deviations, calculating statistical control parameters, and reallocation resources without requiring manual intervention. This automation increases productivity by eliminating delays while the rule-based decision logic simplifies the allocation complexity.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting resource allocation levels based on real-time statistical analysis of process deviations. When excesses are identified, the system automatically modifies resource distribution parameters to eliminate the excesses, improving productivity while using quantitative criteria to manage allocation complexity.
3Measurement precision
If statistical control parameters are set based on standard deviations from mean, then measurement precision is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary calculation of statistical control parameters (mean and standard deviations) during normal operation and stores them for immediate use in deviation detection. This preliminary action eliminates the need to recalculate statistical parameters for each new data point, improving measurement precision while minimizing data processing time.
Solution Approach 2:
The system replaces manual statistical analysis with automated computational algorithms that instantly calculate standard deviations and control parameters from incoming data streams. This substitution of mechanical manual analysis with electronic computation achieves high measurement precision without the time penalty associated with complex manual processing.
Data Source
AI summary
An improved industrial process includes: receiving in a processor a plurality of data items related to an industrial process, each data item being time stamped so that each data item includes time stamp and industrial process data regarding an industrial process occurring at a time; analyzing the plurality of data items in a processor via a plurality of rules, the analyzing identifying deviations of at least one variable of the plurality of data items from a mean value of the variable; setting a statistical control parameter as an achievable quantity for the at least one variable; identifying the plurality of data items where the at least one variable exceeds the statistical control parameter to define at least one excess; and eliminating the at least one excess by shifting resources or altering the process related to the at least one quantity, the shifting or altering being a function of the analyzing of the plurality of data items. Methods related to achievable opportunities for improvement and to identifying contributing factors are also provided.