Overview of Machine Learning Techniques, AI for Business, Basics of Big Data and logistics infrastructure, Preview: Intro to A/B testing simulation, AI for New Logistics Model, Data Science for Customized Logistics, Data Science to improve Logistics Operations.

Big Data Analytics for Reengineering Business Processes, Supply Chain Digitization: Unified view of Demand, Segmentation, Smart S&OP, Smart Execution, Creating your Digital Roadmap, Prioritization Guidelines, Transformation Roadmap, Developing an Effective Solution Architecture.

AI-Driven Logistics Transformation, Lowering Barriers for AI Use, AI in the Organization Structure, Network Planning and Inventory Optimization, Supplier Selection, Transportation Mode Selection, Supply Contracts and Risk Sharing Strategies, Predictive Analytics, Risks with AI, AI Governance in Logistics.

ML in Supply Chains, Recommender Systems, Impact of Recommenders on Logistics, Challenges with Personalization, The Risk Exposure Index, Supply Chain Flexibility, Supply Chain Redundancy, Latest Tools and Techniques, Inventory Management Strategies.

Ethical Issues in AI, Sustainable Supply Chain as a Source of Competitive Advantage, Optimizing Efficiency, Responsiveness and Carbon Emission trade-offs, Short-Term and Long-Term Opportunities to Reduce Carbon Emission, Trends in AI driven Logistics.