Significance of Artificial Intelligence in Carrier Performance Grading, Thus Improving Supply Chain Delivery Efficiency

Carrier performance grading is critical for enhancing supply chain management efficiency, especially in the rapidly evolving e-commerce logistics sector. Traditional grading methods are mainly reliant on retrospective historical data, have limitations like insufficient predictive capabilities, delayed adaptability, and inefficiencies resulting from manual processes This paper examines predictive analytics and artificial intelligence (AI) as solutions to these challenges, focusing on supply chain optimization with an emphasis on carrier performance grading. The research shows an in-depth comparative analysis of two advanced predictive analytics methods which are Random Forest and XGBoost and recommends in terms of predictive accuracy and computational efficiency. Detailed benchmarking results, practical Python implementation examples, and robust statistical evidence are provided to underscore the practical advantages of integrating AI, including reduced forecasting errors, improved capacity utilization, and enhanced resource allocation. At last, the paper concludes by discussing strategic implications and guidelines for implementing AI-driven carrier performance grading systems, illustrating their applicability across diverse logistics-intensive industries as well.