Machine Learning Model for Shipment Delay Prediction

International Journal of Statistics, Econometrics and Data Analysis Applications

AI Enabled Risk Management in International Logistics Networks

International logistics networks are increasingly exposed to operational, financial, geopolitical, customs, cyber, and environmental risks. Effective risk management is therefore essential for reducing shipment delays, controlling logistics costs, and improving supply chain reliability. This study aims to examine how artificial intelligence-enabled classification models can support risk prediction in international logistics networks. For this research, a primary dataset was developed containing 3,000 shipment records with 30 variables, including shipment route, transport mode, cargo value, cargo weight, transit time, delay days, weather risk, political risk, customs risk, port congestion, cyber risk, insurance cost, total logistics cost, and actual risk level. The study applied supervised machine learning methods using a 70% training, 15% validation, and 15% testing split. Three AI-based classifiers—Random Forest, XGBoost, and LightGBM—were used to classify shipment risk into Critical, High, and Medium levels. Model performance was evaluated using accuracy, precision, recall, F1-score, and classification reports. The results show that Random Forest and XGBoost achieved the highest accuracy of 91%, while LightGBM achieved 90% accuracy. Random Forest and XGBoost performed strongly in identifying Critical risk shipments, both reaching an F1-score of 0.97 for the Critical class. LightGBM showed better recall for Medium risk shipments, indicating stronger detection of minority risk cases. Overall, the findings suggest that AI-enabled models can effectively support risk prediction and decision-making in international logistics networks. The study concludes that machine learning can improve proactive risk management, reduce uncertainty, and enhance logistics network resilience.

An Explainable Machine Learning Framework for Green Maritime Logistics Using Dynamic Spatial Congestion Prediction and Relative Fuel Index Modeling

This study proposes an explainable AI approach for predicting localized congestion and vessel fuel demand to support sustainable port logistics, using Automatic Identification System (AIS) data and reveals that historical traffic density was the most important factor contributing to congestion.

Fine-Grained Forecasting of Short-Haul Logistics Volume Based on Time Series Feature Engineering and XGBoost Algorithm

: Addressing the demand for high-frequency response efficiency in modern short-haul logistics networks, this study investigates fine-grained forecasting of short-haul logistics volume. First, historical logistics volume data undergoes preprocessing to screen target routes and eliminate outliers, while a custom function enables scientific segmentation of production periods across dates. Subsequently, periodic decomposition techniques were applied to decompose the raw time series into trend, seasonal, and residual components, confirming the pronounced cyclical patterns in logistics demand and the stationarity of residuals. During modeling, a sliding-window feature matrix was constructed to convert time-series data into supervised learning format. The XGBoost gradient boosting algorithm was employed, with the objective function optimized via second-order Taylor expansion to effectively capture nonlinear characteristics in freight volume sequences. The results show that the model achieves a coefficient of determination of 0.94 on the independent test set, with an average absolute percentage error of only 4%, demonstrating outstanding fitting capability and generalization performance. Ultimately, the model successfully achieves cargo volume extrapolation at 10-minute granularity for the next 24 hours, accurately capturing the bimodal characteristics of cargo volume on specific routes, laying a solid data foundation for subsequent dynamic scheduling optimization.

Predicting Ship Added Mass Coefficient with MACHINE LEARNING: Accuracy, Safety and Cost Savings

Maritime accidents such as capsizing, storm-induced roll resonance, collisions and groundings continue to occur in Bangladesh’s inland and coastal waterways. While these events are usually linked to overloading, weather conditions, and maintenance issues, another important hydrodynamic factor - the Added Mass Coefficient (AMC) is rarely examined. Traditional methods for calculating AMC are too slow for use in real operations. In this study, we explored how different machine learning (ML) models, including Random Forest (RF), Neural Networks (NN), Support Vector Regression (SVR), Linear Regression (LR), and Long Short-Term Memory (LSTM) networks can predict AMC values from basic vessel parameters. Our results show that AMC can be considered not only as a design variable but also as an operational safety parameter. By predicting AMC in advance the models provide a way to support safety actions such as adjusting heading, controlling load distribution and reducing risks in shallow-water navigation. We also suggest that future work should combine real-time data with hybrid approaches to strengthen the reliability of predictions.

LOGISTICS AND TRANSPORTATION: FREIGHT DEMAND FORECASTING USING SARIMA AND XGBOOST

Accurate freight demand forecasting is essential for improving logistics planning and supporting decision-making in e-commerce supply chains. This study compares the performance of two forecasting approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Gradient Boosting (XGBoost)—for predicting daily freight demand measured by transported weight. The research followed the CRISP-DM methodology using the Brazilian Olist public e-commerce dataset. After data preprocessing, exploratory analysis, stationarity testing, and feature engineering, multiple SARIMA and XGBoost models were developed and evaluated using chronological train-test splitting, cross-validation, and Mean Absolute Percentage Error (MAPE). The SARIMA models incorporated seasonal differencing and Box-Cox transformations, whereas the XGBoost models included calendar-based variables, moving averages, and moving standard deviations. The results demonstrate that feature engineering substantially improved predictive performance. The best XGBoost model achieved a MAPE of 3%, considerably outperforming the best SARIMA model, whose predictive accuracy remained limited despite data transformations. These findings indicate that machine learning techniques combined with temporal feature engineering provide superior freight demand forecasts for e-commerce logistics. The proposed approach offers a practical decision-support tool for transportation planning, resource allocation, and operational efficiency while providing a reproducible computational workflow through publicly available source code and processed data.