Client's supply chain organization was building a proof-of-concept application leveraging machine learning to enhance their demand forecasting operations. The goal was to be able to accurately predict demand for products at each of their retail locations using a combination of unstructured and structured data sources such as social media feeds, weather forecasts, market disruptions, and historical trends. We supported the data aggregation, preparation, and analysis of the machine learning model that powered the application. Work resulted in successful proof-of-concept for a select retail location, with improved demand forecasts from model outputs compared to baseline forecasting practices.