LLM Copilot for Manufacturing Equipment Selection

When a new product enters production, every decision matters. Ramp-up, the phase where new products are integrated into existing lines, can make or break delivery timelines. At the heart of this process lies one critical factor: selecting the right automation equipment.

‍

The Challenge

Choosing robots, feeders, and vision systems is not just about ticking boxes. Each decision impacts speed, quality, and cost. Yet, traditional equipment selection support methods often rely on static rules and rigid workflows. Under time pressure, engineers face trade-offs that lead to inefficiencies, quality risks, and costly rework - issues that ripple across the entire value chain.

‍

Our Solution: A Factual-Driven Copilot

Together with the Hong Kong Industrial Artificial Intelligence & Robotics Centre (FLAIR), and WZL RWTH Aachen, we developed a new approach:

A Copilot for Equipment Selection, powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).

What makes it different?

• Guided Decision-Making: The copilot validates all requirements before generating recommendations - transparent and traceable.
• Structured Knowledge Integration: Combines language model capabilities with structured and semi-structured data for precise suggestions.
• Industrial Validation: Tested in real-world scenarios, delivering actionable recommendations for robots, feeders, and vision systems.

‍

Why It Matters

Efficient ramp-up depends on smart equipment choices. By leveraging AI-driven decision support, manufacturers can:

• Minimize integration risks
• Reduce inefficiencies
• Accelerate time-to-market without compromising quality

‍

Proven Results

In tests with an industrial partner:

• 47 prompts analyzed
• 24 cases: Most requirements met
• 20 cases: All requirements fully satisfied

This demonstrates the copilot’s potential to transform equipment selection - making ramp-up faster and more reliable.

‍

The Bigger Picture

This research doesn’t just propose a tool - it signals a shift toward dynamic, transparent, and intelligent decision support in manufacturing. By leveraging RAG and LLMs, we aim to minimize inefficiencies, reduce integration risks, and accelerate time-to-market without compromising standards.

‍

Discover the full insights behind our AI-powered copilot and its impact on automation workflows in the complete publication: Designing an LLM-Based Copilot for Manufacturing Equipment Selection