Intern - PLN IE

Project Title

Agentic AI–Driven Intelligent Planning Ecosystem for Fab Space and Capacity Optimization

Project Description

This internship project focuses on the development of an Agentic AI-enabled planning ecosystem to enhance fab space utilization, tool placement planning, and decision-making across the Planning (PLN) domain. The intern will contribute to the design and evaluation of an intelligent multi-agent framework that supports planning optimization through data-driven analysis and Artificial Intelligence technologies.

The project will explore the use of Agentic AI architecture and Google SDK technologies to coordinate multiple specialized AI agents, including:

• Industrial Engineering (IE) Agent for tool demand forecasting and capacity planning
• Layout Agent for space allocation analysis and constraint evaluation
• Material and Spare Planning Agent for resource planning optimization
• Operations Modelling Agent for scenario simulation and planning insights
• Cost/Capex Agent for investment and cost optimization assessments

Through collaboration with planning stakeholders, the intern will gain exposure to AI-enabled planning methodologies, multi-agent reasoning frameworks, optimization techniques, and large-scale manufacturing planning challenges.

Objective of the Project

The objective of this project is to evaluate how Agentic AI solutions can improve planning effectiveness by enabling coordinated decision-making across multiple planning domains. The project aims to identify opportunities to optimize fab capacity utilization, improve planning alignment, and enhance future-state scenario evaluation through intelligent agent collaboration.

Project Scope

The intern will have opportunities to:

• Study existing planning workflows and data inputs across the PLN domain.
• Develop proof-of-concept Agentic AI workflows for planning optimization.
• Evaluate coordination mechanisms between multiple specialized AI agents.
• Analyze planning trade-offs involving tool demand, factory layout constraints, material requirements, operational scenarios, and capital investment considerations.
• Explore AI-enabled approaches for generating planning recommendations and scenario-based decision support.
• Document findings, recommendations, and improvement opportunities.

Learning Opportunities

The intern will gain hands-on experience in:

• Agentic AI and multi-agent system design
• Generative AI and Large Language Model (LLM) applications
• Manufacturing planning and capacity optimization methodologies
• Operations research and optimization techniques
• Data analytics and scenario modelling
• AI-enabled decision support systems
• Cross-functional collaboration within semiconductor manufacturing environments

Deliverables

• Agentic AI planning ecosystem proof-of-concept or prototype
• Multi-agent workflow design documentation
• Planning optimization analysis and scenario evaluation results
• Recommendations for space utilization, tool placement, and planning improvements
• Final project presentation summarizing findings and future enhancement opportunities

Impact of the Project

This project aims to demonstrate how Agentic AI technologies can contribute to improved planning effectiveness by enabling coordinated, data-driven decision-making across multiple planning functions. The outcomes may provide insights into space optimization opportunities, planning efficiency improvements, and scalable approaches for future intelligent planning ecosystems.

Skillsets Required

• Programming experience in Python, Java, or similar languages
• Knowledge of data analytics, statistics, or optimization techniques
• Familiarity with Artificial Intelligence, Generative AI, LLMs, Agentic AI, or AI-enabled workflows
• Understanding of operations research, industrial engineering, or manufacturing systems is advantageous
• Strong analytical and problem-solving skills
• Effective communication and presentation skills
• Ability to work independently on a project-based learning assignment

Course of Interest

The ideal candidate should be pursuing Computer Science, Artificial Intelligence, Data Science, Industrial Engineering, Operations Research, Manufacturing Engineering, Systems Engineering, or a related field of study.

Duration of Period

The ideal candidate should be able to commit to a full-time internship period of 6 months.

Opportunities for Full Time Employment

Successful completion of the internship may provide opportunities to be considered for future full-time roles, subject to business requirements, performance, and available openings.