Application of statistical process control and Pareto analysis for quality control improvement in cosmetics manufacturing
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Abstract
This study aimed to enhance quality control (QC) in cosmetics manufacturing by addressing critical production defects and inefficiencies through targeted corrective measures. It employed a mixed-methods approach, integrating statistical tools such as Pareto analysis and statistical process control with real-time monitoring systems. Key interventions focused on resolving issues related to raw material mixing, temperature instability during emulsification, and packaging contamination. Data collected from a medium-scale manufacturing facility were analyzed to identify root causes and implement optimized processes. The findings demonstrated significant improvements, including a 50% reduction in defect rates, a 15% decrease in material waste, and a 25% increase in production yield. The integration of advanced monitoring systems enhanced temperature control stability to 95%, ensuring consistent product quality and compliance with ISO 22716 and good manufacturing practices. Pilot testing validated the scalability and effectiveness of these measures under operational conditions. The results underscore the transformative potential of systematic QC interventions in achieving operational excellence, regulatory adherence, and sustainable manufacturing practices. This study provides a scalable framework for QC enhancement in the cosmetics industry and highlights the importance of data-driven approaches for continuous improvement. Despite these promising results, the study was limited by the pilot duration and which may affect generalizability.
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