THE ROLE OF DESCRIPTIVE STATISTICS IN ANALYZING AND STREAMLINING SUPPLY CHAIN PERFORMANCE METRICS

This study explores the role of descriptive statistics in analyzing and streamlining supply chain performance metrics from 2020 to 2024. The research aims to evaluate how statistical tools enhance decision-making in supply chain management, optimize inventory control, and improve operational efficiency. A mixed-methods approach was employed, integrating quantitative analysis of key performance indicators (KPIs) such as inventory turnover, lead time, and order accuracy. Descriptive statistical measures-including mean, standard deviation, and trend analysis-were used alongside correlation and regression techniques to assess performance improvements. Major findings revealed that lead time across product categories decreased from 10 to 6 days, while order fulfillment accuracy increased from 95% to 99%, signifying enhanced efficiency. A strong negative correlation (r = -0.85) was observed between inventory turnover and lead time, suggesting that higher turnover reduces delays. Regression analysis indicated that a one-day increase in lead time leads to a 2.1% decline in customer satisfaction. The overall correlation coefficient (r = 0.88) confirmed the effectiveness of predictive statistical models in supply chain optimization. Based on these insights, it is recommended that businesses integrate automated data analytics, adopt real-time monitoring tools, and enhance statistical training for supply chain professionals to drive further efficiency gains.