Optimizing Coffee Supply Chains: Strategies for Increasing Farmer Income

Abstract

This research addresses the significant imbalance in value distribution within the global coffee supply chain, where farmers typically receive a disproportionately small share of the final retail price. Existing initiatives like Fair Trade have had limited success in fundamentally altering this structure. This thesis applies quantitative optimization techniques, specifically Mixed Integer Linear Programming (MILP) and network analysis, to the Brazilian coffee supply chain to identify strategies for enhancing farmer income. The study analyzes trade data to model the supply network, evaluates the potential for increasing the total Freight on Board (FOB) value by optimizing trade relationships, and explores logistical efficiencies through farmer collectivization using Vehicle Routing Problem (VRP) analysis. Key findings indicate substantial potential to increase the overall FOB value of exports through optimized trade pairings while maintaining network stability. Furthermore, the analysis reveals significant logistical cost savings (over 80%) achievable through farmer cooperation and coordinated transport, such as hub-and-spoke models or optimized VRP routes. The research identifies specific low-performing municipalities as targets for interventions and highlights the prevalence of conditions suitable for forming farmer collectives based on geographic proximity and shared market access. Overall, the work demonstrates that data-driven optimization and coordinated logistics offer promising pathways toward improving value retention at the origin and fostering a more equitable coffee industry.