Enhancing Supply Chain Resilience: A Deep Learning Approach to Late Delivery Risk Prediction

This study introduces an innovative approach to predict late delivery risks, aiming to strengthen supply chain resilience through smart, data-driven strategies. The approach combines clustering using the Elbow method and multiclassification, incorporating advanced deep learning models like Neural Network(NN), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM). The findings highlight the effectiveness of the (CNN-LSTM) model in producing more accurate results, ultimately improving supply chain resilience, boosting customer satisfaction, and enabling proactive risk management.