[AI Autonomous Manufacturing Opens the Future] "3W Innovation... Need to Foster Industrial AI Talent for Korean SMEs"

Interview with Jay Lee, Distinguished Professor at the University of Maryland
"Fundamentally Transforming the Manufacturing Paradigm"
"The Key to Restructuring into a High-Tech, High-Value-Added Industry"
"Urgent Need for Data Foundries and Practice-Oriented Educational Infrastructure"

"A lights-out factory (an unmanned and automated factory) is a worry-free factory, and it is fundamentally changing the manufacturing paradigm through prediction, which is the most important factor."

On June 18 (local time), Jay Lee, Clark Distinguished Professor of Mechanical Engineering and Director of the Center for Industrial AI at the University of Maryland, said in an interview with Asia Economy, "The essence of autonomous manufacturing is the '3W innovation,' which leverages artificial intelligence (AI) to reduce work, waste, and worry," and made these remarks.

Professor Lee, a former Vice Chairman of Foxconn and a globally recognized authority in industrial AI, stated, "Predictive maintenance is one of the core areas of autonomous manufacturing. By detecting equipment failures in advance, it is possible to dramatically reduce downtime and maintenance costs."

He emphasized, "AI and humans should become collaborative partners, not competitors. Autonomous manufacturing is the key to transforming manufacturing into a high-tech, high-value-added industry."

Regarding Korea's transition to autonomous manufacturing, Professor Lee advised, "It is urgent to spread user-friendly AI technology across industries and to establish a field-based data ecosystem that can foster practical industrial AI talent for small and medium-sized enterprises."

The following is a Q&A with Professor Lee.

-AI-driven autonomous manufacturing is spreading worldwide. How do you assess this trend?

▲A 'lights-out factory' means a worry-free factory. The key is predictive technology that responds before problems occur. It is essential to enable advance detection across all manufacturing machines, processes, and systems. This is becoming the new standard for smart manufacturing. Whether it is AI-based 3D printing or unmanned factories, the ultimate goal is to achieve '3W innovation'?reducing work, waste, and worry?through the use of AI. This trend goes beyond simple automation and is transforming the very paradigm of manufacturing. Factories can operate 24 hours a day without human intervention, and both labor costs and error rates are dramatically reduced. AI and robotics are at the core of this advancement, ensuring optimized production schedules, improved precision, and consistent quality.

-In which areas does industrial AI create the greatest value on the manufacturing floor?

▲Predictive maintenance is a representative example. If AI identifies signs of equipment failure in advance and takes action, downtime can be minimized and maintenance costs reduced. In 2006, I led a project applying AI-based predictive maintenance at the Toyota plant in Georgetown, Kentucky. At that time, the compressed air system experienced more than one failure per year on average, but after the project, there was not a single unexpected shutdown in that system.

-Beyond productivity improvement, what additional benefits does AI-driven autonomous manufacturing offer, and what are the main challenges companies face?

▲The core value of AI-driven autonomous manufacturing is that it transforms manufacturing into a high-tech, high-wage sector. This creates a virtuous cycle, attracting more talent into manufacturing. However, there are also barriers. There is resistance to change and high initial investment costs. In addition, large-scale real-time data collection and processing infrastructure is required to enable autonomous learning by machines.

-What kind of data infrastructure and platform strategies are needed for machines to learn autonomously in real production environments?

▲Four technologies are essential: the ability to search, use, analyze, reuse, and manage data; the ability to monitor, diagnose, and predict conditions using various AI algorithms and tools; the ability to manage production schedules and process optimization; and platform capabilities that enable AI to be applied simultaneously at the edge and in the cloud.

-How do you expect autonomous manufacturing to evolve over the next 5 to 10 years?

▲Autonomous manufacturing will fundamentally reshape the manufacturing industry in terms of improved efficiency, cost reduction, and innovation.

-As autonomous manufacturing systems become commonplace, changes are expected in the roles of workers and factories. How should the relationship between human workers and AI be redefined in future factories?

▲In the future, human workers and AI will develop a collaborative, not competitive, relationship. AI will take on repetitive and hazardous tasks, while humans will focus on strategic decision-making, creative problem-solving, and supervision. To achieve this, it is necessary to build 'large-scale industrial knowledge models' that integrate domain expertise with AI. This provides the foundation for intelligently responding to complex on-site problems.

-To what extent has Korea transitioned to autonomous manufacturing, and what strategies and ecosystem development are needed to secure future competitiveness?

▲Korea is actively advancing the field through initiatives such as the AI Autonomous Manufacturing Alliance. To secure future competitiveness, it is essential to develop user-friendly AI technologies tailored to each industry and to establish systems that can rapidly deploy them on the production floor. In addition, it is important to train industrial AI talent for small and medium-sized enterprises and to build practical, data-driven training systems to narrow the technology gap. For example, the University of Maryland operates an industrial AI education platform that uses real factory data. Korea also urgently needs to build data foundries and expand practice-oriented educational infrastructure.