Researchers Develop AI Framework to Optimize Coffee Supply Chains for Cost, Emissions, and Freshness
A new study combines deep learning demand forecasting with multi-objective optimization to improve coffee supply chain management across cost, environmental impact, and product quality. The hybrid CNN-LSTM model achieved 22.87 MAE and 0.90 R² on the Coffee Chain Sales dataset, outperforming existing benchmarks by 12-30%. The framework demonstrates that balanced sustainability policies could reduce emissions by 22.4% with only a 9.9% cost increase while maintaining freshness.
Researchers have developed an integrated two-phase framework addressing fragmentation in coffee supply chain management. The first phase employs a hybrid CNN-LSTM neural network for demand forecasting, tested on the public Coffee Chain Sales dataset with standard 70/15/15 chronological splitting. The model achieved a mean absolute error of 22.87 and R² of 0.90, substantially outperforming the best existing deep learning approaches by approximately 12% and classical forecasting methods by over 30%. In the second phase, forecasted demand feeds into a tri-objective mixed-integer linear programming model that simultaneously minimizes costs, reduces carbon emissions, and maximizes product freshness across a multi-period, multimodal closed-loop supply chain. Freshness is quantified through exponential decay based on inventory age. Using the epsilon-constraint method, the optimization generated 25 Pareto-optimal solutions, with sensitivity analyses revealing that sustainability-focused policies could achieve 22.4% emissions reductions while incurring only 9.9% additional costs.
What's missing
The study's limitations and open questions are not detailed in the abstract, including: the generalizability of the CNN-LSTM model to coffee supply chains beyond the specific dataset used; computational scalability for real-world implementation across complex multi-tier networks; validation through industry case studies or pilot implementations; and how the framework accounts for external disruptions (weather, geopolitical factors) that significantly affect coffee production.
What different sources said
- arXiv cs.AICenter
Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management
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