Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Supply Chain Management: Optimizing Logistics with Artificial Intelligence concentrates on the movement side of the chain — getting goods where they need to be, efficiently. You learn to apply AI to route and fleet optimisation, warehouse operations and picking, last-mile delivery, and network design, working with the operational data logistics generates. The course keeps the focus on the cost-service trade-offs and real constraints that logistics decisions must respect. You finish able to apply AI to a logistics-optimisation problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to supply-chain logistics optimisation — routing, fleet, warehouse and last-mile efficiency across the movement of goods.
📋 Course Objectives
1. Optimise routing and fleet operations.
2. Improve warehouse and picking efficiency.
3. Optimise last-mile delivery.
4. Support network and hub design.
5. Balance cost, speed and service.
👥 Who Should Enroll?
• Logistics and operations professionals
• Supply-chain analysts and planners
• Data scientists in logistics
• Students of operations management
🚀 Key Learning Outcomes
• The ability to apply AI to logistics.
• A logistics-optimisation project.
• An operations-focused 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
Introduction to AI in Supply Chain Management Section 1.1: Overview of Supply Chain and AI
Subsection 1.1.1: Understanding the Supply Chain Key components: Procurement, manufacturing, warehousing, transportation, and distribution. • Challenges in traditional supply chains: Delays, inefficiencies, and lack of real-time visibility.
Data Management in Supply Chain Section 2.1: Understanding Supply Chain Data
Subsection 2.1.1: Types of Data in Supply Chain Structured data: Inventory levels, sales orders, delivery times. • Unstructured data: Supplier communication, customer feedback. • External data: Weather patterns, market trends, and geopolitical events.
AI Applications in Supply Chain Optimization Section 3.1: Demand Forecasting with AI
Subsection 3.1.1: Traditional vs AI-Driven Demand Forecasting Limitations of traditional forecasting methods. • How AI improves accuracy using historical and external data.
AI in Supply Chain Monitoring and Control Section 4.1: Real-Time Monitoring Systems
Subsection 4.1.1: AI for Warehouse Monitoring Automating inventory counts with computer vision. • Real-time alerts for anomalies in warehouse operations.
Ethical and Security Considerations Section 5.1: Data Privacy in Supply Chains
Subsection 5.1.1: Ensuring GDPR and CCPA Compliance Safeguarding customer and partner data.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
