Tool PM Yield Correlation Using Moving Average and CUSUM
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Solution Overview
Problem
Current methods for scheduling tool prevention maintenance (PM) in equipment manufacturing lack effective correlation with product yield, leading to improper maintenance timing and decreased productivity and yield.
Innovation Solution
A method that collects product yield data, calculates yield gaps before and after PM, applies a moving average to reduce noise, and uses cumulative sum charts to identify trends, sending notifications for tools with significant yield changes to inform workers about PM timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prevention maintenance is scheduled without correlating to product yield data, then maintenance activities can be performed, but the timing may be improper leading to decreased productivity and yield
Solution Approach 1:
The system implements feedback by collecting product yield data before and after maintenance events, calculating yield gaps, and using this information to evaluate maintenance effectiveness. This feedback loop enables continuous improvement of maintenance scheduling by correlating maintenance activities with actual product yield outcomes, thereby resolving the contradiction between maintaining reliability and preserving productivity.
Solution Approach 2:
The system enables self-service by automatically collecting yield data, calculating yield gaps, identifying significant maintenance events, and generating notifications without requiring manual intervention. This automated self-evaluation process allows the system to independently determine optimal maintenance timing based on actual performance data, balancing reliability and productivity requirements.
2Measurement precision
If comprehensive yield data collection and analysis is performed to determine optimal maintenance timing, then maintenance effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis process into distinct modular steps: data collection, yield gap calculation, significance determination, continuous trend identification, and notification generation. Each module handles a specific aspect of the analysis, making the overall system more manageable and easier to implement while achieving high measurement precision in maintenance timing.
Solution Approach 2:
The system introduces an intermediary notification mechanism that bridges the gap between complex data analysis and practical maintenance decision-making. The notification system translates complex yield gap analysis and trend identification results into actionable alerts for maintenance personnel, reducing the perceived complexity while maintaining high measurement precision.
3Loss of information
If yield gap calculation and trend analysis are performed for all tools, then accurate maintenance insights are obtained, but processing time and computational resources increase
Solution Approach 1:
The system applies local quality by focusing detailed analysis only on tools that exhibit significant yield gaps or continuous trends, rather than uniformly processing all tools. By identifying and prioritizing tools with notable maintenance impacts, the system reduces overall processing time while maintaining accurate maintenance insights for the most critical equipment.
Solution Approach 2:
The system employs partial action by performing comprehensive yield gap calculation and trend analysis only when necessary - specifically when yield gaps exceed thresholds or continuous trends are detected. This selective approach avoids unnecessary processing of tools with stable performance, reducing time loss while preserving information accuracy for tools that require attention.
Data Source
AI summary
A method for finding the correlation between tool PM (prevention maintenance) and the product yield of the tool is disclosed. The method uses a moving average method to magnify a curve trend that is formed by the product yield data that is captured during a predetermined days before PM and after PM. The magnified curve trend is shown by a Cumulative sum chart. The Cumulative sum chart is analyzed for informing related workers of the effect between the tool PM and the product yield, so as to accurately estimate PM timing. Thereby, via the method, the effect between the tool PM and the product yield may be determined, which serves as an important reference for workers to execute further PM.