Build practical AI demand forecasting skills by combining demand data preparation, machine learning models, demand sensing, and human-AI decision making. Learn how to transform business data into reliable forecasts and support better supply planning decisions.
This Specialization takes you through the complete AI forecasting workflow. You'll learn to clean and transform demand data, engineer predictive features, prevent data leakage, and organize forecast-ready datasets. You'll then evaluate random forests, neural networks, LSTMs, and transformers while considering accuracy, explainability, computing requirements, and deployment trade-offs.
You'll also explore demand sensing using sales, inventory, market, and digital signals to recognize changing demand patterns and forecasting exceptions. Learn how human judgment, AI recommendations, forecast overrides, collaborative planning, and what-if scenarios can support stronger supply decisions.
By the end, you'll be able to prepare forecasting data, evaluate predictive architectures, interpret demand signals, assess human and AI recommendations, and apply scenario-based thinking to practical demand planning challenges.
Applied Learning Project
Through practical, industry-focused projects, learners will prepare demand datasets, engineer predictive features, evaluate random forests and neural networks, and analyze demand signals to identify forecasting exceptions. They will apply human-AI decision-making techniques and what-if scenario analysis to address realistic supply planning challenges, assess forecasting trade-offs, and recommend data-driven strategies for improving demand planning accuracy and operational efficiency.
