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The evaluation of measurement systems has evolved toward structured analytical frameworks collectively known as Measurement System Analysis (MSA). Gauge Repeatability and Reproducibility studies (GR&R) provide a framework to quantify and control variability in measurement processes, but their implementation has often depended on closed source or tools with limited adaptability. This study presents an interactive Shiny application developed in R to support systematic comparison, visualization, and interpretation of GR&R, offering an open and adaptable solution consistent with Industry 4.0 principles. The tool includes classical GR&R and statistical approaches based on design of experiments and analysis of variance (ANOVA). Through interactive data selection and visualization, users can identify variability components, analyze error sources, and ensure methodological transparency. The comparison of approaches showed that ANOVA-based methods improve precision in identifying variability sources, providing more robust insights into measurement errors than classical GR&R. The application was tested in industrial and educational settings, demonstrating its effectiveness in improving measurement accuracy, decision-making reliability, and professional skills development.

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Shiny-Based R Framework for Comparative Repeatability and Reproducibility Analysis in Metrology: Integrating DOE and ANOVA Approaches

  • 会议论文

  • 发表日期：

    2026年

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  • Juliana Garcia-Villada,
  • Carmen E. Patiño-Rodríguez

摘要

The evaluation of measurement systems has evolved toward structured analytical frameworks collectively known as Measurement System Analysis (MSA). Gauge Repeatability and Reproducibility studies (GR&R) provide a framework to quantify and control variability in measurement processes, but their implementation has often depended on closed source or tools with limited adaptability. This study presents an interactive Shiny application developed in R to support systematic comparison, visualization, and interpretation of GR&R, offering an open and adaptable solution consistent with Industry 4.0 principles. The tool includes classical GR&R and statistical approaches based on design of experiments and analysis of variance (ANOVA). Through interactive data selection and visualization, users can identify variability components, analyze error sources, and ensure methodological transparency. The comparison of approaches showed that ANOVA-based methods improve precision in identifying variability sources, providing more robust insights into measurement errors than classical GR&R. The application was tested in industrial and educational settings, demonstrating its effectiveness in improving measurement accuracy, decision-making reliability, and professional skills development.