Machine Learning–Based Late Delivery Risk Prediction for Global Supply Chain Operations Using Random Forest Classification

Author

Sanjana D

Abstract

 Late delivery is one of the major challenges in global supply chain management, leading to customer dissatisfaction, increased operational costs, and service-level agreement (SLA) violations. Traditional logistics systems identify delays only after they occur, limiting proactive decision-making. This research proposes a Machine Learning–Based Late Delivery Risk Prediction System using the APL Logistics Supply Chain Dataset to predict shipment delays before dispatch. The framework includes data preprocessing, exploratory data analysis, feature engineering, and predictive modelling using a Random Forest Classifier. Engineered features such as Shipping Pressure Index, Order Complexity Score, Profit Margin, and Express Shipping indicators were incorporated to improve prediction accuracy. An interactive Streamlit dashboard was developed to visualize logistics performance, predict delivery risk, analyse regional trends, evaluate model performance, and provide operational recommendations. The system enables logistics managers to identify high-risk shipments, prioritize resources, and improve transportation planning. Experimental results demonstrate that the proposed framework effectively predicts late delivery risk while providing interpretable insights into the key factors influencing shipment delays. The developed system transforms traditional reactive logistics management into a proactive, data-driven decision support solution that enhances operational efficiency, customer satisfaction, and overall supply chain performance.

Keywords

Machine Learning, Random Forest, Supply Chain Management, Late Delivery Prediction, Logistics Analytics, Streamlit Dashboard, Predictive Analytics.

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