Researchers Develop AI Framework to Optimize Coffee Supply Chains for Cost, Emissions, and Freshness
A team of researchers has developed a two-phase data-driven framework combining deep learning demand forecasting with multi-objective optimization to manage cost, carbon emissions, and product freshness in coffee supply chains. The framework uses a hybrid CNN-LSTM neural network for demand prediction, feeding results into a mixed-integer linear programming model that balances competing sustainability objectives across a closed-loop, circular supply chain. The study suggests that balanced sustainability policies could cut emissions by 22.4% at a cost increase of only 9.9%, offering a practical trade-off for industry decision-makers.
Researchers from Ankara Yıldırım Beyazıt University, Texas Tech University, and the University of Alabama have published a preprint on arXiv proposing an integrated framework for optimizing circular coffee supply chains. In the first phase, a hybrid CNN-LSTM deep learning model is trained on a public Coffee Chain Sales dataset using a chronological 70/15/15 data split, achieving a mean absolute error of 22.87 and an R² of 0.90—outperforming the best deep learning benchmark by approximately 12% and classical forecasting methods by over 30%. The forecasted demand then feeds into a tri-objective mixed-integer linear programming (MILP) model that simultaneously minimizes cost, minimizes carbon emissions, and maximizes product freshness across multiple time periods and transportation modes. Freshness degradation is modeled using exponential decay based on inventory age, and the epsilon-constraint method is applied to generate 25 Pareto-optimal solutions representing different trade-off configurations. Sensitivity and policy analyses indicate that a balanced sustainability approach can reduce emissions by 22.4% with only a 9.9% cost increase while preserving near-optimal freshness levels. The authors argue that integrating demand forecasting, optimization, and traceability—often treated as separate problems—into a unified framework addresses a key gap in agri-food supply chain management.
What's missing
As a preprint, this work has not yet undergone formal peer review, so findings should be treated as preliminary. The study relies on a single public dataset (Coffee Chain Sales), and generalizability to real-world, industry-scale coffee supply chains with proprietary data structures remains untested. The authors do not address computational scalability of the MILP model for larger supply networks, nor do they validate the framework against actual industry outcomes. The circular recovery and traceability components are modeled theoretically without empirical validation from real closed-loop operations.
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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