Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Supply Chain Management and Logistics Optimization shows how machine learning makes complex supply chains faster, leaner and more resilient. You learn to apply AI across the chain: forecasting demand, optimising inventory and warehousing, planning routes and logistics, and anticipating disruption. The course connects these to the data and systems of modern supply chains and the trade-offs — cost, service, risk — that decisions must balance. Grounded in real logistics problems, it shows AI turning uncertainty into better decisions. You finish able to apply AI to a supply-chain challenge. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in supply chain management and logistics — demand forecasting, inventory optimisation, route planning and end-to-end supply-chain intelligence.
📋 Course Objectives
1. Forecast demand across the supply chain.
2. Optimise inventory and warehousing.
3. Plan routes and logistics with AI.
4. Anticipate and manage disruption risk.
5. Balance cost, service and resilience.
👥 Who Should Enroll?
• Supply-chain and logistics professionals
• Operations and planning analysts
• Data scientists in operations
• Students of supply-chain management
🚀 Key Learning Outcomes
• The ability to apply AI in supply chains.
• A forecasting or optimisation project.
• A resilience-aware logistics approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Reinforcement Learning |
| Covered Tool / Platform | Time-Series Forecasting |
| Covered Tool / Platform | Anomaly Detection |
| Covered Tool / Platform | Simulation Software |
| Covered Tool / Platform | AI Decision Support Tools |
Frequently Asked Questions
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