Master predictive inventory management with AI to improve forecasting, replenishment, warehouse operations, and supply chain resilience.
Learn how AI and predictive analytics turn demand signals and operational data into smarter inventory decisions.
This Specialization develops practical knowledge across the inventory decision lifecycle. You'll evaluate statistical, machine learning, and deep learning forecasting approaches while examining how external signals, feature engineering, forecast uncertainty, and model monitoring influence demand predictions.
You'll then explore AI-informed safety stock, reorder points, replenishment policies, inventory pooling, demand sensing, and multi-echelon optimization. Extend these skills into warehouse operations by evaluating AI-driven slotting, layout optimization, automation, digital twins, computer vision, and robotic execution.
Finally, you'll examine supply chain resilience using scenario planning, Monte Carlo simulation, stress testing, anomaly detection, and AI-supported risk monitoring.
By completing the Specialization, you'll be prepared to evaluate AI-enabled inventory strategies that balance service levels, inventory costs, operational efficiency, sustainability, and supply chain risk.
Applied Learning Project
Learners will work on practical, scenario-based projects involving AI-driven demand forecasting, inventory replenishment, warehouse slotting, and supply chain risk management. Using realistic operational data and business scenarios, they will evaluate predictive models, optimize safety stock and reorder policies, recommend efficient warehouse layouts, and develop resilience strategies to reduce stockouts, control costs, improve fulfillment efficiency, and manage supply chain disruptions.
