A-Tech Gyeongju Automated Production with 970M Won: How an AI Agent Integrated Molding and Robot Logistics

The production of automotive components, especially complex ones like EV busbars, demands flawless precision and continuity. Until recently, A-Tech Gyeongju, a major South Korean manufacturer, faced fragmented post-injection molding processes, which slowed production and impacted quality. The implementation of a comprehensive AI agent-driven automation system, costing 970 million won, allowed for the integration of all stages—from part alignment to inspection and logistics—into a single seamless flow, significantly enhancing overall efficiency.

In modern manufacturing, every break between stages isn't just a delay; it's a potential source of defects, additional costs, and reduced competitiveness. Manual part transfer, visual inspection, unsynchronized machines—these seem like minor issues until you calculate how much time and money is spent correcting errors and dealing with equipment downtime. A solution already exists, allowing production to become a single, unified organism without sacrificing time or quality.

Fragmented Processes: Where Time and Quality Were Lost

A-Tech Gyeongju specializes in manufacturing plastic parts for the automotive industry, including critical components for electric vehicles, such as busbars. The production of these parts involves several post-injection molding stages: product alignment, machining, and thorough inspection. Historically, these stages were performed as separate operations, often manually or using disparate, unintegrated systems.

This approach led to several problems:

• Slow throughput. Transferring parts between machines and manual control created bottlenecks.
• Human error. Visual inspection and manual alignment increased the risk of errors and defects.
• Inefficient logistics. Moving pallets of finished products or blanks required operator intervention, distracting them from more complex tasks.
• Scaling difficulties. Increasing production volumes required a disproportionate increase in staff and equipment, rather than optimizing existing capacities.

Amidst growing demand for EV components, these issues became critical, limiting the company's growth potential and threatening its competitiveness.

The Path to an AI Agent: From Individual Machine Automation to a Unified System

The company already utilized individual automation elements, but they did not solve the problem of comprehensive integration. Existing solutions focused on specific machines or operations but could not provide end-to-end control and coordination between them. A-Tech Gyeongju realized that to achieve truly high productivity and quality, a tool was needed that could not just automate individual links, but unite them into a single, intelligently managed system.

The decision was made to implement an AI agent capable of not only coordinating the work of various robotic systems but also learning, adapting to changes, and making real-time decisions, minimizing human involvement. The goal was to create a "smart factory" where all processes are synchronized and optimized.

How the AI Agent for Manufacturing Was Designed

The developed AI agent was conceived as the central brain of the production line. Its main tasks included:

• Integration of post-molding processes. The agent had to ensure seamless coordination between product alignment, machining, and inspection stages. This meant that decisions about moving a part from one stage to another would be made automatically, based on readiness and quality data.
• Automation of pallet logistics. Autonomous Mobile Robots (AMRs) were deployed to move parts between machines and to storage. The AI agent was to manage this fleet of robots, optimizing their routes and schedules to avoid downtime and congestion.
• Adaptation to the production environment. The system was designed to account for the specific characteristics of A-Tech Gyeongju's production facilities, including equipment layout and flow patterns.
• Comprehensive management. The AI agent had to do more than just control individual robots; it also had to ensure their collaborative work, like a single orchestra, to achieve the overall goal of producing high-quality products with maximum efficiency.

At the core of the system were autonomous navigation and integrated heterogeneous robot fleet management technologies. This allowed the AI agent not only to coordinate robot actions but also to collect production data, analyze it, and make decisions for further optimization.

Implementation: A Phased Transition to Intelligent Manufacturing

The implementation of the automation system at A-Tech Gyeongju was carried out in stages to minimize risks and ensure a smooth transition. The first phase involved a detailed assessment of the existing production line and the development of the AI agent's architecture, taking into account all nuances.

Next, individual modules were gradually integrated:

• Pallet movement automation. Autonomous Mobile Robots (AMRs), managed by the AI agent, were first deployed to optimize logistics. This reduced manual labor and increased the speed of part movement.
• Processing and inspection integration. The alignment, machining, and inspection stages followed, synchronized by the AI agent.
• Staff training. A-Tech Gyeongju employees were trained to work with the new system, allowing them to transition from performing routine operations to monitoring and maintaining robotic complexes.

The project took several months, but improvements in efficiency were noticeable even during intermediate stages. The company actively participated in the process, providing feedback and helping to adapt the system to its unique needs.

Results: Increased Productivity and Quality

The implementation of the AI agent led to a transformation of A-Tech Gyeongju's production processes. The company expects significant increases in productivity and improvements in product quality, especially for critical EV components. Automation not only reduced manual labor but also minimized errors, ensuring consistently high quality at all stages.

How to Apply This to Your Business

The A-Tech Gyeongju case demonstrates that AI agents can be a key success factor in manufacturing, especially where high precision, speed, and integration of complex processes are required. If you face similar challenges, here's where you can start:

• Identify bottlenecks. Pinpoint production stages where delays, manual labor, or a high percentage of defects occur. These areas are ideal candidates for automation.
• Assess integration opportunities. Consider whether disparate processes (e.g., post-molding, processing, inspection, packaging) can be combined into a single, automated flow managed by an AI agent.
• Consider autonomous logistics. If material or finished product movement requires significant effort, implementing AMRs controlled by an AI agent can significantly optimize this process.
• Start with a pilot project. Don't try to automate everything at once. Choose one, most problematic area and implement an AI agent there to assess its effectiveness and adapt it to your needs.

If this case sounds like what's happening in your company, our manager can help: he'll analyze your business and niche for free and point out where an AI agent would bring a real result in your case. Message the manager