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Coursera

AI-Powered Demand Forecasting and Inventory

via Coursera

Overview

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Build practical skills in AI-powered demand forecasting and inventory optimization to make faster, more accurate supply chain decisions. This course shows you how artificial intelligence, machine learning, integrated data, and autonomous planning can help reduce stockouts, excess inventory, carrying costs, and planning delays. You’ll explore demand sensing, probabilistic and hierarchical forecasting, promotional lift, new-product planning, sentiment analysis, product life cycles, and regional forecasting strategies. You’ll then apply AI to dynamic safety stock, multi-echelon inventory optimization, automated replenishment, inventory placement, obsolescence detection, lead-time management, supplier risk, reverse logistics, and sustainable operations. You’ll also evaluate cloud-native platforms, digital twins, APIs, explainable AI, simulations, and low-latency data pipelines that support scalable autonomous planning. By the end, you’ll be able to analyze AI-generated recommendations, balance cost and service levels, improve inventory performance, and outline a phased roadmap for deploying and scaling autonomous planning across markets.

Syllabus

  • Building the Foundations of AI-Driven Planning
    • Build a practical foundation in autonomous supply-chain planning by examining machine learning, integrated data, demand sensing, probabilistic forecasting, promotional lift, product life cycles, sentiment analysis, and global forecasting strategies.
  • Optimizing Inventory with AI Technology
    • Apply AI to dynamic safety stock, multi-echelon inventory, automated replenishment, supplier risk, reverse logistics, and sustainable operations while evaluating cloud architecture, digital twins, explainable AI, and global implementation roadmaps.

Taught by

TSV Learning

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