Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management

Researchers developed a hybrid CNN-LSTM deep learning model for coffee supply chain demand forecasting, achieving 90% accuracy and outperforming benchmarks by 12-30%. This forecasting feeds a multi-objective optimization system that simultaneously minimizes costs and emissions while maximizing product freshness in circular supply chains, demonstrating that sustainability policies can reduce emissions by 22.4% with minimal cost overhead.

This research bridges a critical gap in agri-food supply chain management by integrating demand prediction with sustainability optimization. The coffee industry, characterized by complex global logistics and sensitivity to product degradation, represents an ideal testbed for data-driven frameworks that address multiple competing priorities simultaneously. The hybrid CNN-LSTM architecture captures both temporal patterns and sequential dependencies in sales data more effectively than traditional forecasting methods, establishing a foundation for more accurate inventory planning.

The innovation extends beyond prediction into operational decision-making through multi-objective optimization. By modeling product freshness as exponential decay based on inventory age, the framework quantifies a previously difficult-to-optimize variable. The Pareto frontier approach reveals trade-offs between cost, environmental impact, and quality, providing supply chain managers with evidence-based policy options rather than forced compromises.

For the broader agri-food sector, this framework demonstrates that sustainability need not be costly. The finding that emissions can drop 22.4% while increasing costs only 9.9% challenges the common perception of sustainability as a luxury feature. This has implications for regulatory compliance with emerging carbon accounting standards and consumer demand for transparent supply chains.

The closed-loop circular model incorporates recovery operations, aligning with growing EU regulations on extended producer responsibility. Supply chain operators and technology vendors serving agri-food sectors should monitor adoption of hybrid deep learning architectures combined with optimization solvers. Future applications may extend to perishable goods broadly, including fresh produce, dairy, and prepared foods, where freshness constraints similarly drive operational complexity and waste.

• →CNN-LSTM demand forecasting achieved 0.90 R² on coffee sales data, outperforming existing methods by 12-30%.
• →Multi-objective optimization reduced emissions 22.4% while increasing costs only 9.9%, proving sustainability can be economically viable.
• →Exponential decay modeling of product freshness enables quantitative optimization of quality metrics alongside cost and emissions.
• →Pareto-based optimization provides supply chain managers with evidence-based trade-off options rather than binary choices.
• →Framework applicability extends beyond coffee to broader perishable goods sectors with similar freshness constraints.