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Machine Learning for a More Efficient Supply Chain

Drew Arenth, Benjamin Fels, and Suvrit Sra of Macro-Eyes in the U.S. are applying a statistical machine learning approach to the immunization supply chains of health facilities in Tanzania that accurately and continuously predicts demand to ensure the right vaccines and levels are being stocked. Currently, vaccine supply is largely fixed or driven by depleted stocks. This leaves children unable to be vaccinated due to stock outs at clinics, as well as often high levels of waste, which could both be overcome by better forecasting vaccination needs for individual clinics. In Phase I, they worked with an NGO and the Ministry of Health to access routinely-collected daily vaccination data from 710 health facilities spread across Tanzania. These data were then used to train algorithms to identify predictive patterns that were tested on independent datasets. This led to a model that could accurately forecast future vaccine consumption. In Phase II, they will work out how best to integrate their approach as an automated component within the existing supply chain infrastructure in Tanzania and develop tools and train advocates to demonstrate its value and encourage implementation and adoption.

More information about Health Systems Strengthening: Ensuring Effective Health Supply Chains (Round 19)

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