APPLYING REGRESSION ANALYSIS TO IDENTIFY KEY FACTORS AFFECTING SUPPLY CHAIN EFFICIENCY AND DELIVERY TIMES

This study investigates key factors affecting supply chain efficiency and delivery times from 2020 to 2024 using regression analysis. The primary objective is to identify the impact of transportation management, inventory optimization, and supplier reliability on delivery performance. A quantitative research approach was adopted, analyzing data from manufacturing, retail, and logistics sectors. Multiple regression models revealed a strong negative correlation between inventory levels and delivery times (r = -0.89, p < 0.01), confirming that efficient inventory management accelerates deliveries. Supplier lead times significantly influenced delivery consistency (β = -0.65, p < 0.05), while transportation cost reductions correlated with improved efficiency (β = -0.58, p < 0.05). The overall correlation coefficient for key supply chain factors and delivery times was r = -0.82, indicating a strong relationship between operational improvements and delivery efficiency. The study concludes that optimizing supply chain components enhances efficiency, reducing delivery times from 7.5 days in 2020 to 6.3 days in 2024. Recommendations include AI-driven predictive analytics for demand forecasting, supplier performance management strategies, and real-time transportation tracking for improved logistics.