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EATME: An R package for EWMA control charts with adjustments of measurement error
Li-Pang Chen1, Cheng-Kuan Lin1
1Department of Statistics, National Chengchi University, Taipei, Taiwan, ROC.
Plos One
|October 3, 2024
Summary
This paper introduces the EATME R package for Exponentially weighted moving average (EWMA) control charts, adjusting for measurement error to improve process monitoring accuracy.
Area of Science:
- Statistical Process Control
- Quality Management
- Industrial Statistics
Background:
- Measurement error can significantly impact the effectiveness of traditional control charts.
- Existing methods may not adequately correct for these errors in continuous or binary data.
- Accurate process monitoring is crucial for maintaining product quality and operational efficiency.
Purpose of the Study:
- Introduce the EATME R package for EWMA control charts with adjustments for measurement error.
- Provide tools to correct for measurement error effects in statistical process control.
- Enhance the accuracy of detecting out-of-control processes.
Main Methods:
- Development of an R package, EATME, incorporating EWMA control charts.
- Implementation of functions to adjust for measurement error in continuous and binary variables.
- Inclusion of functions for synthetic data generation, control limit coefficient determination, and average run length estimation.
Main Results:
- The EATME package effectively corrects for measurement error in EWMA control charts.
- Corrected control charts demonstrate improved accuracy in detecting process deviations.
- Numerical studies validate the package's functionality and performance.
Conclusions:
- The EATME R package offers a robust solution for statistical process control in the presence of measurement error.
- It provides enhanced accuracy for monitoring both in-control and out-of-control processes.
- The package facilitates better quality management through reliable process monitoring.
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