  Accurate demand forecasting is crucial for optimizing supply chain
management. Traditional methods often fail to capture complex patterns from
seasonal variability and special events. Despite advancements in deep learning,
interpretable forecasting models remain a challenge. To address this, we
introduce the Multi-Channel Data Fusion Network (MCDFN), a hybrid architecture
that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory
networks (LSTM), and Gated Recurrent Units (GRU) to enhance predictive
performance by extracting spatial and temporal features from time series data.
Our rigorous benchmarking demonstrates that MCDFN outperforms seven other
deep-learning models, achieving superior metrics: MSE (23.5738%), RMSE
(4.8553%), MAE (3.9991%), and MAPE (20.1575%). Additionally, MCDFN's
predictions were statistically indistinguishable from actual values, confirmed
by a paired t-test with a 5% p-value and a 10-fold cross-validated statistical
paired t-test. We apply explainable AI techniques like ShapTime and Permutation
Feature Importance to enhance interpretability. This research advances demand
forecasting methodologies and offers practical guidelines for integrating MCDFN
into supply chain systems, highlighting future research directions for
scalability and user-friendly deployment.
