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

This study examines the role of statistical sampling in improving quality control and ensuring consistency in supply chain operations. The research employs a mixed-methods approach, incorporating quantitative analysis from supply chain performance reports (2020–2024) and qualitative insights from industry experts. Key findings reveal that statistical sampling led to a 64.7% reduction in defect rates (χ² = 18.6, p = 0.002), improved supplier performance with a correlation coefficient of 0.78 (R² = 0.61), and achieved significant cost savings (ANOVA: F = 5.62, p = 0.003). Furthermore, companies implementing advanced predictive sampling models experienced a 17% greater reduction in quality control costs compared to conventional methods. The study concludes that statistical sampling is a strategic necessity for enhancing quality, reducing operational inefficiencies, and achieving supply chain sustainability. It recommends integrating artificial intelligence, improving data governance, and developing adaptive sampling models to optimize supply chain performance in dynamic global markets.