Build stronger inventory decisions with AI-powered demand forecasting and predictive analytics. In this course, you’ll learn how predictive inventory engines combine internal records with external signals such as search trends, weather, economic indicators, and supplier information to anticipate changing demand.

You’ll compare statistical, tree-based, and deep learning forecasting models and examine how feature engineering, ensemble approaches, and forecast uncertainty influence demand predictions. Using examples from retail, supply chain, healthcare, and energy, you’ll explore how predictive insights can support timely inventory and supply chain actions. You’ll also examine the operational requirements behind reliable AI, including data preparation, model monitoring, explainability, privacy, and bias. Designed for learners interested in AI-driven inventory and supply chain decision-making, this course connects forecasting techniques with practical operational considerations. By the end, you’ll be able to evaluate forecasting models and signals, apply predictive insights to inventory scenarios, assess responsible AI requirements, and outline a practical path from a focused predictive use case to wider adoption.