Today’s modern supply chains are complex global networks of manufacturers, suppliers, logistics, and retailers, which requires sophisticated methods of sensing and adapting to customer demand, fluctuations in raw material availability, and external factors such as holidays, events, and even weather. The repercussions of not predicting these variables correctly can be costly, resulting in either over or under-provisioning and leading to wasted investment or poor customer experiences. To help foresee the future, companies are using machine-learning to analyze time-series data and provide accurate forecasts that help them to reduce operating expenses and inefficiencies, ensure higher resource and product availability, deliver products faster, and lower costs.