📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI in Supply Chain Management and Logistics Optimization Course, from foundations to a certified capstone project.
Outline
The shift from Traditional to "Cognitive" Supply Chains • Mathematics of Optimization: Linear programming and beyond • Business Case: ROI of AI in Logistics
Outline
Handling fragmented supply chain data (ERP, IoT, and External APIs) • Preprocessing time-series data for logistics • Feature engineering for demand and supply variables
Outline
Supervised learning for lead-time prediction • Unsupervised learning for supplier segmentation and clustering • Reinforcement Learning for the "Traveling Salesman Problem" in logistics
Outline
Tuning models for extreme seasonality and market shifts • Hyperparameter optimization for logistics KPIs • Evaluation metrics: MAPE, RMSE, and Bullwhip effect reduction
Outline
Building real-time logistics dashboards • Integrating AI models with existing TMS (Transport Management Systems) • Scaling MLOps for multi-location warehouse systems
Outline
Bias in algorithmic sourcing and procurement • Transparency in automated delivery decisions • Sustainability and the "Green AI" approach in logistics
Outline
Case Study: Last-mile delivery optimization in urban India • Cold chain management using IoT and AI • Automating invoice and customs clearance with NLP
Outline
End-to-End AI Logistics Solution • Implementation of a demand forecasting and route optimization model using real datasets
Earn government-registered certification in AI in Supply Chain Management and Logistics Optimization Course
e-Certificate and e-Marksheet issued on successful completion.
