Supply Chain Management in AI Age

Module 1: Introduction to AI in Supply Chain

• Overview of traditional vs. AI-powered supply chains
• Key drivers of AI adoption: speed, complexity, and customer expectations
• Technologies enabling transformation: ML, NLP, computer vision, big data
• Real-world examples from Amazon, Maersk, and Unilever

Module 2: AI-Powered Demand Forecasting

• How AI improves accuracy over traditional statistical models
• Demand sensing using real-time market and social data
• Dynamic forecasting in volatile markets
• Use case: AI in seasonal and promotional forecasting in FMCG

Module 3: Smart Inventory Optimization

• AI for dynamic inventory management across multi-echelon networks
• Balancing cost, demand variability, and service levels
• Use of reinforcement learning and simulations
• Case study: Retail chain reducing stockouts by 30% with AI

Module 4: AI in Procurement and Sourcing

• Spend analytics and supplier performance prediction
• Smart contract management with NLP and automation
• Autonomous sourcing bots and tender evaluation
• Example: AI-powered negotiation tools used by global CPG firms

Module 5: Logistics and AI-Driven Route Optimization

• Real-time route planning using traffic, weather, and capacity data
• AI in fleet management and drone deliveries
• Autonomous vehicles and smart warehousing
• Case study: DHL and FedEx AI logistics enhancements

Module 6: AI and Robotic Process Automation (RPA)

• Streamlining repetitive supply chain tasks (e.g., order entry, invoicing)
• RPA bots integrated with AI for intelligent decision-making
• Cognitive automation in back-office operations
• Example: RPA use in reducing procurement cycle time

Module 7: Digital Twins and Supply Chain Simulations

• Creating a real-time virtual model of the supply chain
• Simulating bottlenecks, demand surges, and capacity planning
• AI-enhanced visibility and proactive issue resolution
• Case study: Siemens and GE's use of digital twins in production logistics

Module 8: AI in Risk Management and Resilience Planning

• Predicting disruptions: geopolitical, climate, supplier failure
• AI-driven risk scoring and alerts
• Building supply chain resilience through scenario modeling
• Real-world example: AI in COVID-19 disruption response

Module 9: Ethics, Bias & Governance in AI Supply Chains

• Addressing algorithmic bias in supplier selection and demand models
• Ensuring transparency, fairness, and auditability
• Regulatory compliance and ethical AI principles
• Frameworks: Responsible AI, Model Explainability, Data Privacy

Module 10: Building the AI-Enabled Supply Chain Roadmap

• Strategic planning: maturity assessment, goals, and KPIs
• Change management and stakeholder alignment
• Upskilling and reskilling supply chain teams
• Implementation blueprint and success measurement