AI for Supply Chain and Logistics
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to AI in Supply Chain
Overview of supply chain challenges and AI opportunities
Digitization and automation trends
AI vs. traditional supply chain systems
Module 2: Data Foundations for Supply Chain AI
Data sources: ERP systems, sensors, IoT, customer orders
Cleaning and preprocessing structured and time-series data
Feature engineering for supply chain KPIs
Module 3: Demand Forecasting with Machine Learning
Time series analysis for order forecasting
Regression models and LSTM for seasonal trends
Evaluating forecast accuracy (MAPE, RMSE)
Module 4: Inventory Optimization with AI
Dynamic stock level prediction
ABC analysis with clustering
Reducing stockouts and overstock with predictive modeling
Module 5: Logistics and Route Optimization
Shortest path and TSP problem-solving
Real-time delivery routing with AI
Last-mile delivery prediction and constraints handling
Module 6: Warehouse Automation and Robotics
AI vision for product identification and sorting
Pathfinding and scheduling in warehouses
Robotics-assisted fulfillment systems
Module 7: Risk Management and Disruption Prediction
Predicting supply chain disruptions (weather, strikes, demand spikes)
Supplier risk assessment using ML models
Scenario planning with simulations
Module 8: Real-Time Monitoring & Control with AI
IoT data analytics for fleet and inventory
AI dashboards and live anomaly alerts
Control towers and centralized AI systems
Module 9: Ethics, Sustainability & AI Governance
Reducing carbon footprint using AI
Ethical sourcing and responsible logistics
Data privacy and compliance (GDPR, ISO standards)
Module 10: Capstone Project
Choose from:
Forecasting demand and inventory for a retail chain
Designing a dynamic delivery route optimizer
Building a supply chain risk alert system
Submit a working solution with a visual dashboard and final report
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