PREDICTING LOGISTICS DELAYS FOR RESILIENT SUPPLY CHAINS  USING DEEP LEARNING

Panupong Wanjantuk; Ruth Banomyong

Proceedings of ICLT 2025

PREDICTING LOGISTICS DELAYS FOR RESILIENT SUPPLY CHAINS USING DEEP LEARNING

Panupong Wanjantuk; Ruth Banomyong

Department of Computer Engineering, Khon Kaen University, Thailand; Center of Excellence in Connectivity, Thammasat Business School, Thammasat University, Thailand

International Conference on Logistics & Transport 2025, Tokyo, Japan, pp. 169-178

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Abstract

Purpose: Building resilience in logistics systems is essential to navigating disruptions, reducing risk, and ensuring operational continuity. Despite growing disruptions, existing delay prediction models often lack interpretability and adaptability. This study addresses that gap by developing and explaining a deep learning-based model to predict delivery delays, thereby supporting proactive logistics decision-making and enhancing supply chain resilience. Design/methodology/approach: We use a real-world logistics dataset containing over 32,000 hourly records from a Southern California network collected between 2021 and 2024. Data features span traffic congestion, ETA variation, port activity, vehicle behavior, and IoT sensor data. After preprocessing and feature selection, we trained 10 Multi-Layer Perceptron (MLP) models with varying depths and dropout rates. Binary classification was performed using the delay_probability variable, with class imbalance handled through class weighting in the loss function. Model performance was evaluated via accuracy, precision, recall, and AUC. The best-performing model was further explained using Captum’s Integrated Gradients method to identify key contributing features. Findings: The optimal MLP model achieved 72.8% accuracy and high recall, effectively identifying delayed deliveries. Using Captum’s Integrated Gradients, input features were ranked by their contribution to model predictions. The top features included ETA variation, port cong

Keywords

Resilient Logistics; Delay Prediction; Deep Learning; Explainable AI; Supply Chain Analytics

Citation

Panupong Wanjantuk; Ruth Banomyong (2025). PREDICTING LOGISTICS DELAYS FOR RESILIENT SUPPLY CHAINS USING DEEP LEARNING. Proceedings of the International Conference on Logistics & Transport (ICLT 2025), Tokyo, Japan, pp. 169-178.