Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time-series forecasts. Recently, based on Amazon Forecast, we helped one of our retail customers achieve accurate demand forecasting, within 8 weeks. The solution improved the manual forecast by an average of 10% in regards to the WAPE metric. This leads to a direct savings of 16 labor hours monthly. In addition, we estimated that by fulfilling the correct number of items, sales could increase by up to 11.8%. In this post, we present the workflow and the critical elements to implement—from proof of concept (POC) to production—a demand forecasting system with Amazon Forecast, focused on challenges in the retail industry.
Background and current challenges of demand forecasting in the retail industry
The goal of demand forecasting is to estimate future demand from historical data, and to help store replenishment and capacity

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