MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model

TL;DR

This paper introduces MCDFN, an explainable hybrid deep learning model combining CNN, LSTM, and GRU for improved supply chain demand forecasting, outperforming existing models and providing interpretability through explainable AI techniques.

Contribution

The paper presents a novel hybrid architecture, MCDFN, that effectively captures complex patterns in demand data and enhances interpretability with explainable AI methods.

Findings

MCDFN outperforms seven deep-learning models with lower error metrics.

MCDFN achieves a Theil's U statistic of 0.1181, indicating superior forecasting accuracy.

Explainability techniques like ShapTime improve model interpretability.

Abstract

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 comparative 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%). Theil's U statistic of 0.1181 (U<1) of MCDFN indicates its superiority over the naive forecasting approach, and a 10-fold cross-validated statistical paired t-test with a p-value of 5% indicated no significant difference between MCDFN's predictions and actual values. 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.