UNIT 1:
Overview of Machine Learning Techniques
Overview of Machine Learning Techniques
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
UNIT 2:
Creating your Digital Roadmap
Prioritization Guidelines
Developing an Effective Solution Architecture
Smart S&OP, Smart Execution
Big Data Analytics for Reengineering Business Processes
Supply Chain Digitization: Unified view of Demand
Smart S&OP and Smart Execution_Case
UNIT 3:
AI-Driven Logistics Transformation
Lowering Barriers for AI Use
AI in the Organization Structure
Network Planning and Inventory Optimization
Transportation Mode Selection
Supply Contracts and Risk Sharing Strategies
AI Governance in Logistics
UNIT 4:
Impact of Recommenders on Logistics
Challenges with Personalization
Latest Tools and Techniques
Inventory Management Strategies
UNIT 5:
Sustainable Supply Chain as a Source of Competitive Advantage
Responsiveness and Carbon Emission trade-offs
Short-Term and Long-Term Opportunities to Reduce Carbon Emission
Trends in AI driven Logistics