Enhancing Cost-Efficiency and Effectiveness in USAID's Food Aid Supply Chain Operations in Ethiopia.

This article develops a data-driven optimization model of the U.S. Agency for International Development Bureau for Humanitarian Assistance and the legacy Office of Food for Peace's (USAID/BHA/FFP) food aid supply chain, focusing on Ethiopia as a major recipient country. It evaluates how extending advance demand information (ADI)—the lead time between demand submission and delivery—and implementing commodity prepositioning strategies (CPS)—the strategic stocking of inventory—affect cost-efficiency and delivery performance. Using historical data from 2011 to 2016 and a rolling horizon algorithm that simulates dynamic operational decisions, the study finds that longer ADI durations (four to six months) significantly improve demand fulfillment and on-time delivery rates while reducing costs, whereas CPS is most beneficial when ADI is short. The research highlights trade-offs between cost and service levels, recommending tailored combinations of ADI and CPS to optimize USAID's food aid operations under varying real-world conditions.