Abstract
This study presents a deep learning-based approach for forecasting product demand and predicting shipping delays within large-scale supply chain operations. Using a real-world dataset sourced from Kaggle comprising over 180,000 transaction-level records and 53 variables, two artificial neural network (ANN) models were developed: one for sales per customer (demand forecasting) and another for shipping delay estimation. After feature selection and preprocessing, eight key numerical variables including Product Price, Benefit per Order and Late Delivery Risk were standardized and used to train the models. The ANN architecture consisted of two hidden layers (64 and 32 neurons, respectively), with ReLU activation and dropout regularization. Experimental results show that the demand forecasting model achieved excellent performance, with a mean absolute error (MAE) of 11.72, root mean squared error (RMSE) of 23.94 and an R2 score of 0.97, indicating high predictive accuracy. In contrast, the shipping delay prediction model exhibited moderate results, with an MAE of 0.81, RMSE of 1.02 and R2 of 0.72, reflecting the underlying complexity of logistics dynamics. Visual analyses, including scatter plots, residual histograms and correlation heatmaps, confirmed strong linear relationships (e.g., Sales per Customer and Product Price: r = 0.78) and highlighted challenges due to heteroscedasticity and outliers. The study demonstrates the viability of ANN models in operational forecasting while identifying areas for enhancement particularly in delay prediction, where inclusion of temporal and categorical features could yield improved results. These findings contribute to data-driven supply chain optimization using modern AI techniques.
Keywords: Artificial Neural Networks, Demand Forecasting, Deep Learning, Shipping Delay Prediction, Supply Chain Analytics.