Estimating True Demand at Hunger Relief Organizations with Predictive Modeling

Significant uncertainty exists both on the supply and demand side of food bank operations, but the demand side has received significantly less attention in the literature. In this study, we used data from a food bank branch to characterize the behavior of their partner agencies. Of the three predictive models considered, random forest was applied to estimate the number of persons served by the partner agencies, which is then converted to food demand. Our bottom-up approach can be replicated for all branches in a food bank’s network to obtain their true demand.