GPMS TRANSPORTAL APPLIED AI COURSES

This course covers the application of AI to streamline semiconductor supply chain management and optimize the semiconductor manufacturing process. Students      will learn how AI can predict supply chain issues, improve production scheduling, and reduce costs in semiconductor manufacturing.

Level: Intermediate

Prerequisites: Basic understanding of supply chain management and manufacturing processes.

Assessments: Micro Assessment weekly, Full Assessment in the final week.

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Week 1 - AI in Semiconductor Supply Chain Overview

Learning Outcome

Students will learn about the importance of supply chain management in semiconductor manufacturing and how AI can improve its efficiency.

1.1 Introduction to the semiconductor supply chain.
1.2 Key challenges in semiconductor manufacturing and logistics.
1.3 Role of AI in supply chain optimization.

Practical Component
Simulate supply chain processes and identify areas where AI can be applied for optimization.

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Week 2 - AI for Demand Forecasting and Inventory Management

Learning Outcome

Understand how AI can be used to predict demand and manage inventory more efficiently in semiconductor manufacturing.

2.1 Using AI for demand forecasting in semiconductor production.
2.2 Machine learning models for inventory management and stock optimization.
2.3 Reducing waste and minimizing stockouts.

Practical Component
Implement a machine learning model to forecast demand for semiconductor components and manage inventory levels.

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Week 3 - AI-Driven Production Scheduling and Resource Allocation

Learning Outcome

Learn how AI can optimize production schedules and resource allocation in semiconductor factories.

3.1 AI for optimizing production schedules.
3.2 Resource allocation and machine learning in semiconductor fabs.
3.3 Minimizing downtime and improving throughput using AI.

Practical Componenet
Use AI tools to simulate production scheduling and resource allocation in a semiconductor factory.

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Week 4 - AI for Quality Control and Defect Detection in Manufacturing

Learning Outcome

Students will learn how AI models can identify defects during semiconductor manufacturing and improve product quality.

4.1 AI-based defect detection systems.
4.2 Machine learning for real-time quality control.
4.3 Predictive quality assurance in semiconductor manufacturing.

Practical Componenet
Use computer vision and AI algorithms to detect defects in semiconductor components.

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Week 5 - AI for Yield Optimization and Process Improvement

Learning Outcome

Learn how AI can enhance yield and process efficiency in semiconductor manufacturing.

5.1 Machine learning for yield prediction.
5.2 Process optimization using AI.
5.3 Statistical process control (SPC) with AI.

Practical Component
Apply machine learning algorithms to predict and optimize yield in a semiconductor manufacturing environment.

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Week 6 - Capstone Project and Assessment

Learning Outcome

Students will work on a project where they apply AI tools to improve semiconductor supply chain efficiency and manufacturing optimization.

6.1 Full-cycle implementation of AI in the supply chain and manufacturing process.
6.2 Peer assessments and final review of the project.
6.3 Final project presentations.

Practical Component
Students will present their solutions for optimizing the semiconductor manufacturing supply chain using AI.

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