Amazon Pharmacy Increases Forecast Accuracy and Reduces Manual Efforts Using AWS Supply Chain

Amazon Pharmacy needed to forecast prescription demand across all 50 states to support same-day delivery, but separate forecasting processes run by different teams caused longer cycle times, higher resource needs, and limited data granularity. It deployed AWS Supply Chain Demand Planning, which uses machine learning on historical sales data and current information like open orders to generate daily-level forecasts. The solution cut roughly 5 hours per week of manual planning time (data entry and calculations) and achieved a daily Mean Absolute Percent Error of 5%, described as 50% better than the industry-standard MAPE target of 10% or less. It also improved scalability, inventory agility, and staffing/labor planning precision.

AWS is Amazon's cloud platform, used across pharma for genomics compute, SageMaker/Bedrock model training, and healthcare-and-life-sciences-specific services like Amazon HealthOmics.

This sits in the Build stage of the AI use-case lifecycle: Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.