ENEOS Materials Adopts ChatGPT Enterprise for Manufacturing Productivity

ENEOS Materials has deployed ChatGPT Enterprise across its entire workforce to address labor shortages and rising operational costs in the Japanese manufacturing sector. The adoption has resulted in over 90% of employees using the tool weekly and the creation of more than 1,000 custom GPTs to optimize specialized industrial workflows.

Operational Efficiency and Workforce Adoption

ENEOS Materials achieved rapid organizational integration of AI by prioritizing security and ease of use. By utilizing ChatGPT Enterprise, the company met internal cybersecurity requirements for handling proprietary information while enabling employees to generate results using natural language in Japanese without requiring coding skills.

Key adoption metrics include:

  • Employee Engagement: Over 90% of employees use ChatGPT at least weekly.
  • Workflow Improvement: 80% of employees reported significant improvements in their workflows during the pilot phase.
  • Custom Tooling: The organization has developed over 1,000 custom GPTs to handle specific business needs.

Accelerating Technical Research with Deep Research

Deep research capabilities within ChatGPT have reduced the time required for complex technical investigations from months to minutes. This is particularly impactful for the Process Development and Engineering Department, which manages a plant in Hungary and uses the AI to overcome language barriers and technical complexity.

Measurable outcomes of deep research deployment include:

  • Language Barrier Removal: Hungarian sources are comprehensively searched and translated into precise Japanese.
  • Time Reduction: Investigations that previously took months are now completed in minutes.
  • Technical Calculation: Complex chemical engineering calculations and analyses that once took half a day are now finished in minutes.

Optimizing Plant Design and Safety

The Engineering department utilizes a custom GPT specifically designed for plant design based on company standards. This tool generates optimized specifications based on inputs such as flow rate, pipe diameter, pressure loss, fluid type, and material requirements.

This implementation improves both speed and safety:

  • Design Speed: Confirming material corrosion risks and design baselines now takes seconds rather than considerable effort.
  • Risk Mitigation: The AI flags material-selection risks during the design phase, strengthening overall reliability and safeguards.
  • Cost Efficiency: The tool enables optimal plant design by cross-referencing internal standards with the AI's domain knowledge.

Streamlining Human Resources and Training

ChatGPT Enterprise has transformed HR operations by automating the analysis of employee training feedback and the creation of data aggregation tools.

Key HR improvements include:

  • Analysis Speed: Training evaluation tasks that previously required 1-2 hours of manual work are now completed in 20 seconds.
  • Data Aggregation: The time required for data aggregation was reduced by approximately 90% through a tool built by an HR employee with no prior coding experience.
  • Continuous Improvement: AI-driven systems evaluate training based on established educational frameworks to refine content.

Future Integration and Scaling

ENEOS Materials intends to move beyond general AI assistants to integrate internally trained AI models directly into manufacturing equipment. The long-term goal is to enable natural language control on the shop floor, allowing employees to guide and optimize production through everyday language.

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