Machine Learning Model for Shipment Delay Prediction
Abstract
The fast pace of development of e-commerce has elevated the timely delivery as a characteristic element of customer satisfaction and logistics performance. However, there are still delays in shipment because of uncontrollable factors like traffic, weather, operational bottlenecks and network inefficiencies. To overcome this issue, this paper derives a Machine Learning Model of Shipment Delay Prediction to combine refined shipment data, operational time and contextual logistics data to predict the probability of delay at an early phase. Based on the previous studies of real-time delay prediction, proactive risk assessment, as well as ML-based logistics optimization, the suggested framework will integrate feature engineering, supervised learning models (Random Forest, XGBoost, CatBoost, Logistic Regression), and a multi-stage prediction process. This methodology is focusing on interpretability, prediction on each shipment processing step, and scalability to the logistic operations. The experimental findings indicate that the gradient-boosting models are rather consistent in terms of their performance (high ROC-AUC scores and higher recall in the delay class). This study adds a useful and empirical methodology, which can be adopted by logistics teams to predict disruptions, make sound-informed routing, and enhance service reliability.