How LLMs Are Used in Manufacturing in 2025

The Trend: LLMs on the Shop Floor

Manufacturing has always struggled with information bottlenecks. Manuals that no one reads, knowledge scattered in Excel sheets, tribal expertise held by senior operators. The arrival of LLMs offers a different approach: instead of asking people to find and format the right data, you let natural language tools surface it instantly.

Research confirms this shift. Studies on the use of LLMs in industrial settings highlight four main applications:

• Automating documentation and reporting (turning raw logs into structured insights)
• Supporting QA and compliance (quick interpretation of specs, standards, test data)
• Decision support for engineers (calculations, troubleshooting guides, parameter suggestions)
• Training and onboarding (interactive Q&A instead of static manuals)

The key idea: LLMs are not replacing operators — they’re sitting alongside them, as copilots.

Where LLMs Perform Best in Manufacturing

The technology isn’t equally powerful everywhere. Based on both research and early adopters like ENEOS, there are clear sweet spots where language models already outperform traditional workflows:

1. Data-heavy, low-value tasks
   Collecting, searching, and formatting reports. What used to take hours of manual cross-referencing now takes minutes.

2. Knowledge transfer
   Bridging generational gaps on the shop floor. A junior operator can ask the system “why does this machine alarm at 1200 rpm?” and get a context-aware answer that previously required tracking down a veteran engineer.

3. Cross-language communication
   Factories are global, but technical papers, manuals, and supplier documents are often locked in different languages. LLMs shine at instant translation while preserving domain-specific terminology.

4. Process support under uncertainty
   When facing a novel material, condition, or customer spec, operators can use LLMs to quickly surface known risks, troubleshooting steps, or case histories — reducing the guesswork.

Case From Japan - OpenAI Big Deployment

ENEOS Materials, a leading producer of specialty rubbers and advanced materials, recently rolled out ChatGPT Enterprise across its entire workforce. In a matter of months, 80% of employees reported faster workflows. HR cut research tasks by 90%. Engineers reduced months of analysis to minutes. And perhaps most striking — employees themselves created more than 1,000 custom GPTs to handle specialized tasks like corrosion risk calculations or translating Hungarian research papers into Japanese.

This isn’t just another case of “AI hype.” It’s a working example of how large language models (LLMs) can become everyday assistants inside a factory environment, not abstract tools for data scientists.

Why It Matters

The real competitive edge isn’t that a chatbot exists in your plant. It’s that workers no longer waste their energy on low-value searches and reporting. Freed from repetitive work, they can focus on process improvements, customer needs, and innovation.

ENEOS Materials shows what happens when this shift scales: adoption isn’t limited to IT teams or managers — it becomes embedded into how engineers, HR staff, and plant operators actually work.

LLMs in manufacturing are not futuristic add-ons. They are already proving themselves in environments where information friction used to slow everything down. The winners will be the manufacturers who don’t just pilot this technology, but integrate it into the daily rhythm of their operations — making the AI as ordinary and indispensable as a wrench on the shop floor.

About MDCplus

Our key features are real-time machine monitoring for swift issue resolution, power consumption tracking to promote sustainability, computerized maintenance management to reduce downtime, and vibration diagnostics for predictive maintenance. MDCplus's solutions are tailored for diverse industries, including aerospace, automotive, precision machining, and heavy industry. By delivering actionable insights and fostering seamless integration, we empower manufacturers to boost Overall Equipment Effectiveness (OEE), reduce operational costs, and achieve sustainable growth along with future planning.