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

arXiv: https://arxiv.org/abs/2405.15598 · HTML: https://mlsanity.com/paper/2405.15598/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology](https://mlsanity.com/paper/1809.03006/) (2018)
- [Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach](https://mlsanity.com/paper/1706.06279/) (2017)
- [Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology](https://mlsanity.com/paper/1901.04028/) (2019)
- [Modeling Documents with Deep Boltzmann Machines](https://mlsanity.com/paper/1309.6865/) (2013)
- [QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network](https://mlsanity.com/paper/2307.12906/) (2023)
- [Big Dataunicodex2013Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques](https://mlsanity.com/paper/2307.12971/) (2023)

## Built on by (most cited first, 3 of 3)

- [Foundation Models for Demand Forecasting via Dual-Strategy Ensembling](https://mlsanity.com/paper/2507.22053/) (2025)
- [Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models](https://mlsanity.com/paper/2508.13057/) (2025)
- [Optimizing Multi-Tier Supply Chain Ordering with LNN+XGBoost: Mitigating the Bullwhip Effect](https://mlsanity.com/paper/2507.21383/) (2025)

## Abstract (arXiv)

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.