| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 3 Week |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $56 |
| Tools | Python TensorFlow PyTorch Keras Scikit-learn Jupyter Notebook Google Colab Hugging Face |
About the AI for Supply Chain Management: Optimizing Logistics with Artificial Intelligence Course
AI for Supply Chain Management: Optimizing Logistics with Artificial Intelligence is a 10-week program designed for M.Tech and M.Sc students, as well as professionals in BFSI, IT services, and consulting.
The course delves into AI applications such as forecasting, inventory management, and transportation logistics, and teaches participants to develop and implement AI models to optimize supply chains.
Program Highlights
• Comprehensive coverage of AI for Supply Chain Management from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Artificial Intelligence
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Artificial Intelligence
• Career-oriented training for academic and professional growth in Artificial Intelligence
Course Curriculum
Module 1: 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.
Module 2: 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.
Module 3: 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.
Module 4: 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.
Module 5: 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.
Tools, Techniques, or Platforms Covered
Python TensorFlow PyTorch Keras Scikit-learn Jupyter Notebook Google Colab Hugging Face
Real-World Applications
- Apply AI for Supply Chain Management skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Artificial Intelligence competencies
- Solve industry-relevant problems using AI for Supply Chain Management methodologies and tools
- Contribute to open-source projects and collaborative research in Artificial Intelligence
- Prepare for competitive examinations, interviews, and professional certifications in Artificial Intelligence
Who Should Attend & Prerequisites
- Students pursuing degrees in Artificial Intelligence, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Artificial Intelligence roles
- Researchers and academicians looking to adopt modern techniques in Artificial Intelligence
- Entrepreneurs, freelancers, and self-learners interested in practical Artificial Intelligence knowledge
Certification
