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technologyreview.com·3h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
TL;DR
The coffee supply chain faces challenges in demand forecasting, optimization, and sustainability, often addressed separately. A hybrid CNN-LSTM model was developed for demand forecasting, integrated with multi-objective optimization to manage costs, emissions, and freshness.
✦ Why It Matters
Engineers can leverage this integrated framework to enhance sustainability and efficiency in complex supply chains.
Key Takeaways
- Implement hybrid models for demand forecasting.
- Integrate optimization techniques for sustainability.
- Utilize public datasets for model training.
- Focus on balancing cost and freshness in supply chains.
Full Summary
Coffee supply chains are complex due to their geographically dispersed production and the need for high-quality, fresh products. Traditional approaches to demand forecasting, optimization, and sustainability have been siloed, leading to inefficiencies.
A two-phase integrated framework was developed, utilizing a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model for accurate demand forecasting. This model was applied to the public Coffee Chain Sales dataset, achieving significant improvements in forecasting accuracy.
Additionally, multi-objective optimization was employed to balance cost, emissions, and product freshness. Results indicated enhanced sustainability metrics, demonstrating the framework's effectiveness in real-world applications.
This integrated approach can serve as a model for other agri-food supply chains.