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This case study, developed in the Standardization and Quality Control 2025-1 course, analyzed the capping and sealing process in beverage production under Quality 4.0. Using SIPOC, key variables were defined: defective caps (discrete) and closing turns (continuous). Data collection stage enabled BI control charts, p-charts, and Gage R&R by attributes with risk analysis method for assess measurement reliability. From 1,978 caps across 56 batches with 3 operators, high defect rates linked to raw material variability, poor inspection, and unstandardized procedures were found. Corrective actions included stricter inspections, supplier renegotiation, and process standardization. Integrating R-Studio with Power BI enabled real-time dashboards for defect monitoring, supporting data-driven decisions. This approach demonstrates how statistical methods and BI tools enhance quality control, offering potential for predictive analytics, anomaly detection, and broader industrial applications.

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Quality Control in Beverage Production – Cap Defects Analysis Using an Integration of R-Studio in Power BI

  • 会议论文

  • 发表日期：

    2026年

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  • Espitia Rodriguez Arcesio,
  • Carmen E. Patiño-Rodríguez

摘要

This case study, developed in the Standardization and Quality Control 2025-1 course, analyzed the capping and sealing process in beverage production under Quality 4.0. Using SIPOC, key variables were defined: defective caps (discrete) and closing turns (continuous). Data collection stage enabled BI control charts, p-charts, and Gage R&R by attributes with risk analysis method for assess measurement reliability. From 1,978 caps across 56 batches with 3 operators, high defect rates linked to raw material variability, poor inspection, and unstandardized procedures were found. Corrective actions included stricter inspections, supplier renegotiation, and process standardization. Integrating R-Studio with Power BI enabled real-time dashboards for defect monitoring, supporting data-driven decisions. This approach demonstrates how statistical methods and BI tools enhance quality control, offering potential for predictive analytics, anomaly detection, and broader industrial applications.