EATME: An R package for EWMA control charts with adjustments of measurement error

Li-Pang Chen ¹, Cheng-Kuan Lin ¹

⁰Department of Statistics, National Chengchi University, Taipei, Taiwan, ROC.

Plos One +

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October 3, 2024

View abstract on PubMed

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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