| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet from NSTC |
| Fee | ₹5499 / $59 |
| Tools | Python Pandas Scikit-learn PyTorch Reinforcement Learning Time-Series Forecasting Anomaly Detection Simulation Software AI Decision Support Tools |
About the AI in Supply Chain Management and Logistics Optimization Course
The AI in Supply Chain Management and Logistics Optimization course dives deep into how Artificial Intelligence solves the "last-mile" problem and inventory bloat. The curriculum moves beyond basic spreadsheets into the world of Predictive Analytics and Autonomous Decision-Making.
Through a structured curriculum and hands-on approach, you will gain comprehensive expertise in applying machine learning to real-world datasets. From supplier risk assessment to real-time logistics planning, this course bridges the gap between technical AI foundations and practical industry application.
"The global supply chain is no longer just a physical network—it is a data-driven ecosystem. The ability to predict demand, automate warehouses, and optimize routes in real-time is the new gold standard."
The program integrates:
- Demand forecasting and inventory optimization
- Route and logistics optimization via Reinforcement Learning
- Warehouse automation and intelligent sorting
- Supplier risk management and monitoring
- Ethical and sustainable AI in logistics operations
The goal is not to turn logistics managers into data scientists or engineers into supply chain planners. It is to build informed interdisciplinary capability at the intersection of operations management and artificial intelligence.
Aim
AI in Supply Chain and Logistics sits at the intersection of:
- Resilience — AI detects disruptions from geopolitical or weather events before they halt production
- Cost efficiency — route optimization alone reduces fuel and transport costs by 15–20%
- Sustainability — reducing empty miles and optimizing inventory supports corporate ESG goals
- India's National Logistics Policy — as India targets logistics costs below 10% of GDP, AI professionals are at the forefront
AI-driven supply chain systems are already deployed in e-commerce, cold chain management, manufacturing, and third-party logistics. Yet many organizations still rely on fragmented data and manual planning that cannot keep pace with market volatility. Professionals who bridge AI capabilities with operational expertise are in high demand across every logistics vertical.
Program Highlights
Many courses address either generic AI theory or basic logistics management in isolation. This course refuses that split by combining industry-focused AI training with India-specific case studies and a heavy emphasis on measurable business impact. The capstone project reinforces this by requiring participants to deliver a fully functional demand forecasting and route optimization system using real datasets.
Course Curriculum
• Use time-series analysis for precise demand forecasting
• Optimize inventory to eliminate stockouts and overstock
• Apply Reinforcement Learning for route optimization
• Implement intelligent warehouse automation algorithms
• Integrate AI models with Transport Management Systems
• Build an end-to-end AI logistics optimization solution
Module 1 — Foundations of AI in Supply Chain
- The shift from Traditional to "Cognitive" Supply Chains
- Mathematics of Optimization: Linear programming and beyond
- Business Case: ROI of AI in Logistics
Module 2 — Data Engineering & Feature Pipelines
- Handling fragmented supply chain data (ERP, IoT, and External APIs)
- Preprocessing time-series data for logistics
- Feature engineering for demand and supply variables
Module 3 — Model Architecture & Methods
- Supervised learning for lead-time prediction
- Unsupervised learning for supplier segmentation and clustering
- Reinforcement Learning for the "Traveling Salesman Problem" in logistics
Module 4 — Training & Optimization
- Tuning models for extreme seasonality and market shifts
- Hyperparameter optimization for logistics KPIs
- Evaluation metrics: MAPE, RMSE, and Bullwhip effect reduction
Module 5 — Deployment & MLOps
- Building real-time logistics dashboards
- Integrating AI models with existing TMS (Transport Management Systems)
- Scaling MLOps for multi-location warehouse systems
Module 6 — Ethics & Responsible AI
- Bias in algorithmic sourcing and procurement
- Transparency in automated delivery decisions
- Sustainability and the "Green AI" approach in logistics
Module 7 — Industry Integration & Case Studies
- Case Study: Last-mile delivery optimization in urban India
- Cold chain management using IoT and AI
- Automating invoice and customs clearance with NLP
Module 8 — Capstone Project
- End-to-End AI Logistics Solution
- Implementation of a demand forecasting and route optimization model using real datasets
Tools, Techniques, or Platforms Covered
Python Pandas Scikit-learn PyTorch Reinforcement Learning Time-Series Forecasting Anomaly Detection Simulation Software AI Decision Support Tools
Real-World Applications
The expertise gained in this course applies directly to e-commerce giants, 3PL (Third-Party Logistics) providers, manufacturing plants, and retail chains. Participants will be prepared to implement systems that handle thousands of variables to ensure the right product reaches the right place at the lowest cost and with the least environmental impact.
Who Should Attend & Prerequisites
This course is particularly suited for:
- Logistics & Supply Chain Managers seeking digital upskilling
- Industrial Engineers focused on process optimization
- Data Analysts moving into the supply chain domain
- Students looking to enter India's high-growth logistics sector
Certification
