تقرير
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model
العنوان: | MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model |
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المؤلفون: | Jahin, Md Abrar, Shahriar, Asef, Amin, Md Al |
سنة النشر: | 2024 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2405.15598 |
رقم الأكسشن: | edsarx.2405.15598 |
قاعدة البيانات: | arXiv |
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