Learn how to detect changing demand, evaluate forecasts, and make informed supply planning decisions using demand sensing and human-AI collaboration. This course is designed for supply chain professionals, demand planners, business analysts, and learners interested in AI-driven demand forecasting.

You'll explore how sales, inventory, market, and digital signals improve short-term demand sensing when historical forecasts fall behind changing conditions. Learn to identify forecasting exceptions, evaluate model recommendations, recognize human bias, and assess forecast overrides. Discover how human-AI decision making and cross-functional consensus planning support better forecasting decisions. Apply what-if scenario analysis to evaluate supply disruptions, service levels, financial constraints, and operational trade-offs. You'll also examine practical demand sensing cases, explore a demand sensing maturity roadmap, and understand how random forests combine decision trees to support forecasting. By the end of the course, you'll be able to evaluate demand signals, analyze forecasting exceptions, assess human and AI judgments, and apply scenario planning to supply decisions. What makes this course distinctive is its integration of demand sensing, human judgment, AI recommendations, and collaborative decision making.