Forecasting improvements for better reproductive health and family planning operations in global health supply chains

The aim of this project is to develop innovative machine-learning models for forecasting and
subsequently managing contraceptives inventories, and as such improve the performance of
reproductive health and family planning supply chains. The models will be empirically routed in and
validated (pilot tested) using data from Cote d'ivoire. The intention is to produce generalisable
knowledge that may then be applied to other developing countries.