This repository supports the research article titled "Demand Forecasting for Supply Chain Management Using Ensemble Deep Learning", published in the International Journal of Information Technology (Springer Nature). The journal is indexed in Scopus, and the article investigates and compares various machine learning and deep learning models for accurate demand forecasting in supply chain management.
📄 DOI: https://doi.org/10.1007/s41870-024-02157-6
Supply chain management requires accurate demand forecasting to plan procurement, inventory, and delivery efficiently. This research focuses on evaluating and improving demand forecasting using a range of statistical and deep learning models, including:
- Classical models: ARIMA, SARIMA
- Deep learning models: RNN, LSTM, GRU, BLSTM
- Enhanced models: LSTM with Ensemble Learning
The paper explores ensemble learning in two variations:
- Without model pruning: Averaging all LSTM models
- With model pruning: Removing underperformers and averaging the top models
Experiments were conducted using a public dataset from the University of Chicago. Notably, the LSTM model enhanced through ensemble learning with model pruning achieved a very low RMSE of 9.26, demonstrating the potential of this method in improving prediction accuracy for customer demand.
The dataset used in this study is publicly available and can be accessed here:
🔗 Dominick's Finer Foods Dataset - University of Chicago
- Comparative analysis of traditional and deep learning models
- Implementation of ensemble learning to enhance LSTM forecasts
- Introduction of model pruning to boost ensemble efficiency
- Practical implications for supply chain forecasting and planning
If you use or reference this work, please cite the original paper:
Zishan Ahmad, et al. (2024). Demand Forecasting for Supply Chain Management Using Ensemble Deep Learning. Innovations in Systems and Software Engineering.
https://doi.org/10.1007/s41870-024-02157-6