Designing a Forecasting and Reordering System with Modern AI Techniques

Building a forecasting and reordering system that planners actually trust requires more than picking the right model. It requires clean data, sensible features, robust evaluation, and clear handoffs to the people who manage exceptions. This course walks through those choices in the order they actually arise in a real project. You will work through written design exercises that mirror how a small operations or data team would plan a forecasting and reordering system. The emphasis is on the practical tradeoffs that decide whether the system becomes a trusted tool or another dashboard that nobody opens. What you'll learn: - Prepare sales history, promotions, and event data for time-series forecasting - Compare modeling approaches including classical methods, gradient boosting, and modern deep learning options - Engineer features for seasonality, promotions, holidays, and supplier behavior - Build evaluation routines that measure forecast accuracy in operationally meaningful ways - Translate forecasts into reorder recommendations using safety stock and service level targets - Design human-in-the-loop interfaces so planners can override and explain decisions when needed The course progresses from data preparation to model design, then into evaluation, reorder logic, and human integration. A capstone written exercise asks you to draft a one-page design for a forecasting and reordering system for a specific business and product category. This course is designed for beginners with some software or data background, including data scientists entering operations, supply chain analysts exploring AI tools, and students of business analytics. No prior supply chain experience is required. The course treats the system as a design problem you can reason about on paper.