A supply chain that anticipates risk and shifts in demand make the delivery of physical goods more efficient, ensuring that resources serve more people in need. A learning supply chain can also serve as an early warning system for epidemics and reveal significant shifts in how disease impacts different groups or geographies
macro-eyes is working with PATH, ministries of health, and district leadership. macro-eyes machine learning technology is analyzing historical supply chain data, bio-surveillance data, and public data to predict shifts in utilization and recommend corresponding stock levels and vaccine selection for the upcoming months.
The goal: maximize childhood vaccination coverage and minimize vaccine wastage with precise predictions of utilization, translated into vaccine deliveries that accurately anticipate the number of children who will arrive at sites to be vaccinated.
macro-eyes will deploy human-in-the-loop machine learning to engage front line caregivers and gather information from critical health workers. Health worker insight on populations and demand, conveyed via text message, will programmatically augment the analysis of supply chain and immunization data.
Direct engagement increases the accuracy and precision of prediction, can resolve gaps in the data, and empowers champions at the point of care. The predictive supply chain for health will constitute one of the first deployments of machine learning – specifically human-in-the-loop machine learning – for global health.
Health supply chains are increasing in complexity and scale. Global coverage for basic childhood vaccines has reached a record 86%, but there has been a parallel increase in vaccine wastage, decreasing resource efficiency.
Vaccine stock-outs compound the problem by wasting opportunities for immunization that can resonate throughout a community. macro-eyes technology creating the predictive supply chain for vaccines is breaking the link between higher rates of immunization and increased wastage.