No technical or AI background is required.

By the end of this course, participants will be able to:

• Understand how AI is transforming end-to-end supply chains
• Identify key AI technologies used in supply chain management
• Distinguish between traditional, digital, and intelligent supply chains
• Apply AI concepts to forecasting, planning, logistics, and procurement
• Understand predictive, prescriptive, and autonomous decision-making
• Evaluate AI use cases and readiness within their organizations
• Recognize risks, limitations, and ethical considerations of AI
• Develop a roadmap for future-ready, AI-enabled supply chains

This course is designed for:

• Supply Chain and Operations Managers
• Logistics, Warehousing, and Distribution Professionals
• Procurement and Strategic Sourcing Managers
• Planning and Demand Forecasting Teams
• Digital Transformation Leaders
• Operations Excellence and Continuous Improvement Teams
• IT and Data Professionals supporting supply chains
• Executives and Decision Makers overseeing supply chain strategy

The course uses a practical, interactive, and forward-looking approach, including:

• Instructor-led presentations
• Industry case studies and best practices
• Group discussions and scenario analysis
• AI-enabled supply chain use-case demonstrations (conceptual)
• Interactive workshops and breakout sessions
• Short videos and future trend discussions
• Team exercises focused on strategy and decision-making

The emphasis is on understanding, application, and strategic impact, not software coding.

Day 1 – The Changing Supply Chain Landscape & AI Foundations

Topics:

• Evolution of supply chains:
• Traditional → Digital → Intelligent supply chains
• Key global challenges:
• Volatility, disruptions, sustainability, customer expectations
• Introduction to Artificial Intelligence:
• AI vs Automation vs Advanced Analytics
• Machine Learning, Predictive Analytics, Optimization (conceptual)
• Role of data in modern supply chains
• Digital twins and real-time visibility
• From reactive to predictive supply chain management

Case Study:

• Supply chain failures and lessons learned from recent global disruptions

Group Exercise:

• Mapping current vs future supply chain maturity

Day 2 – AI Applications Across the Supply Chain

Topics:

• AI in Demand Planning & Forecasting
• Inventory optimization and replenishment using AI
• AI in production and capacity planning
• Intelligent logistics and transportation optimization
• AI-powered warehousing and automation
• Procurement and supplier risk analytics
• Predictive maintenance and asset performance
• Use of AI for sustainability and carbon footprint reduction

Case Studies:

• AI-driven demand sensing
• Intelligent logistics routing and cost optimization

Workshop:

• Identifying high-impact AI use cases in participants’ supply chains

Day 3 – Strategy, Risks & The Future Supply Chain

Topics:

• Building an AI-enabled supply chain strategy
• Data readiness and organizational capabilities
• Change management and skills transformation
• Human + AI collaboration in decision-making
• Cybersecurity, data privacy, and ethical AI considerations
• Limitations, risks, and common misconceptions of AI
• Future trends:
• Autonomous supply chains
• Cognitive planning systems
• AI-driven resilience and self-healing networks
• Roadmap for AI adoption in supply chain organizations

Final Group Exercise:

• Developing a high-level AI Supply Chain Transformation Roadmap

Course Wrap-Up:

• Key takeaways
• Action planning
• Future skills for supply chain professionals

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.