Summary of Mcdfn: Supply Chain Demand Forecasting Via An Explainable Multi-channel Data Fusion Network Model, by Md Abrar Jahin et al.
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model
by Md Abrar Jahin, Asef Shahriar, Md Al Amin
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Multi-Channel Data Fusion Network (MCDFN), a hybrid deep learning architecture that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU) to improve demand forecasting in supply chain management. The MCDFN is designed to capture complex patterns from seasonal variability and special events, outperforming seven other deep-learning models in a comparative benchmarking study. The paper reports superior metrics: MSE (23.5738), RMSE (4.8553), MAE (3.9991), and MAPE (20.1575%). Additionally, the MCDFN demonstrates superiority over naive forecasting approaches and actual values, as indicated by Theil’s U statistic of 0.1181 and a 10-fold cross-validated statistical paired t-test with a p-value of 5%. To enhance interpretability, the paper applies explainable AI techniques like ShapTime and Permutation Feature Importance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better predict how much stuff we need to make or buy in advance. It’s important because if we don’t have enough, customers might not get what they want on time. Traditional methods are not good at handling changes caused by things like holidays or weather. The new way, called MCDFN, combines different techniques from deep learning to make better predictions. It was tested against other methods and did really well. The results show that MCDFN is a big improvement over just using simple forecasting methods. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Lstm » Mae » Mse
