DAY- I
Industry 4.0
· Digital Twins
· Automation and Application
· Advance Manufacturing Technologies
· Foundations of Smart Manufacturing
Supply Chain 4.0
· Block-chain, IoT and AI and their impact on the supply chain
· Supply Chain Sustainability
Introduction to AI in Supply Chain Management
· Automation & AI in supply chain: history & evolution
· AI-enabled supply chain management benefits
· Basic principles of AI: GenAI, ML, Data Science
Fundamental AI Concepts in Supply Chain Management
· Discover how to approach AI implementation in supply chain management. Cover everything from data collection, identifying use cases, and developing AI solutions.
· Preparing your supply chain for AI
· Identifying AI solutions & selecting appropriate tools
· Integrating AI with supply chain processes
Data Analysis Principles for Supply Chain
· Learn to identify the necessary steps to prepare for implementing your AI
· Data analysis in AI supply chain management
· Overview of data preprocessing techniques
· Exploratory data analysis (EDA) & supply chain datasets
· Skillset and mindset sourcing
Case study #1: Negotiating With A Chatbot: A Walmart Procurement Case Study
DAY – II
Enhancing Decision-Making with AI Applications in Demand Forecasting
· Introduction to AI-driven demand forecasting
· Demand variability management
· Integrating AI with demand planning
Inventory Optimization with AI
· Use AI to identify issues in inventory management, propose effective solutions, and implement actions to optimize inventory processes.
· Using demand forecasting insights to optimize inventory levels
· Automated replenishment
· AI-driven inventory tracking and monitoring solutions
· Integrating AI with existing systems
Leveraging AI for Transport Network Optimization
· AI in network optimization
· Solutions for consolidation in transportation
· Personalized network optimization solutions with AI
· Case study
AI in network optimization
· Solutions for consolidation in transportation
· Personalized network optimization solutions with AI
· Case study
Ethical considerations in AI for supply chain management
· Data privacy & security concerns
· Regulations & compliance
· Introducing ethical data analysis principles
Interpreting AI-driven results
· Determining success: goal, data size, tolerance
· Demonstrating value & ROI
· Actionable recommendations
· Communication strategies for stakeholders