Managing a medical supply chain in low- and middle-income countries can mean navigating a landscape prone to extreme and unexpected disruptions. In Sierra Leone, for instance, external forces ranging from an attempted military coup and an infectious disease outbreak to a widespread electricity outage can complicate public health logistics.
The consequences are severe. Despite a national government initiative dedicated to providing free medical care and essential supplies to pregnant women and children under five, Sierra Leone has one of the highest maternal mortality rates in the world, at 717 deaths per 100,000 live births, explains Hamsa Bastani, an operations researcher and statistician at the Wharton School.
A major driver is not always a lack of medicine but a failure to get the right supplies to the right place at the right time, says Bastani. Some clinics end up overstocked while others run dry.
To address that mismatch, Bastani, computer scientist Osbert Bastani, and Ph.D. candidate Angel Tsai-Hsuan Chung partnered with Sierra Leone’s government to build a low-cost, decision-support system that uses machine learning to forecast demand and optimize how medicines are allocated.
Following a pilot rollout in five districts, the researchers found a 19% increase in consumption of allocated medical products in treated areas, a proxy for improved access. Their findings are published in Nature.
