ELCNN-BiLSTM: A Hybrid Deep Learning Model for Timeliness Prediction in Cross-Border E-Commerce Logistics

Abstract

This paper suggests a Bidirectional LSTM (ELCNN-BiLSTM) model, which is an E-Commerce Logistics CNN-based model of intelligent decision-making and timeliness prediction in the cross-border logistics processes. The data to be used in the study was gathered within a global e-commerce logistics system of a cosmetics retailer with 1,25, 000 delivery records that have 24 spatio-temporal and operational characteristics, such as the shipment weight, their origin, destination, the distance of the route, the time in customs, and the time in the warehouse. The formulated task is a binary classification task to forecast whether a delivery is on-time or delayed, with an on-time threshold parameter being delivery within +/-1 day of the expected arrival. Preprocessing was performed on the basis of alignment of the timestamps, normalisation, treatment of 3.4% missing values by interpolation, and resampling to address a 1:1.7 ratio of the class imbalance with SMOTE. The ELCNN-BiLSTM architecture proposed is a combination of spatial feature extraction with convolutional layers and long-term temporal dependencies with the bidirectional LSTM layers that allow adaptive acquisition of logistics patterns on routes and time zones. Benchmarking of the model was done against LSTM, CNN-LSTM, Att-GRU, and XGBoost baselines on measurements of Accuracy, Precision, Recall, F1-Score, PR-AUC and ROC-AUC. Findings reveal that the suggested model has a better predictive power with 95.4 % accuracy, 93.9 % precision and 96.3 % of ROC-AUC, which are significantly higher than other approaches in all the most important measures. The ability and robustness of the model to generalise and statistical significance tests were statistically proven through statistical tests and k-fold cross-validation. The study presents a powerful data-oriented model of predicting supply chain delays in real-time, which can improve the reliability of supply chains and help to make intelligent decisions in the global e-commerce business.