HOW STATISTICAL SAMPLING IMPROVES QUALITY CONTROL AND ENSURES CONSISTENCY IN SUPPLY CHAIN OPERATIONS

This study investigates the role of statistical sampling in enhancing quality control and ensuring consistency in supply chain operations. Using a mixed-methods approach, quantitative data from supply chain performance reports (2020-2024) were analyzed alongside qualitative insights from industry professionals. The findings reveal a significant reduction in defect rates from 5.0% in 2020 to 3.2% in 2024, with a corresponding increase in cost savings from $2.5 million to $5.5 million. Regression analysis confirms a strong negative correlation (R² = 0.89) between statistical sampling implementation and operational costs, while ANOVA results (F(4, 95) = 16.32, p < 0.001) indicate substantial supplier performance improvements. The overall Pearson correlation coefficient (r = 0.92, p < 0.001) underscores the effectiveness of statistical sampling in streamlining quality control, reducing costs, and enhancing supplier reliability. It is recommended that organizations integrate AI-driven predictive analytics, standardize sampling protocols, adopt hybrid sampling methods, and leverage cloud-based quality control systems to optimize supply chain efficiency.