Leveraging Predictive Analytics to Enhance Supply Chain and Logistics Efficiency: Evidence from Indonesia National E-commerce

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.