At the 'AIM with POSTECH 2024' held on the 24th,
47% of 145 lighthouse manufacturing plants around the world used AI.
“400 million won in cost savings… AI autonomous manufacturing increases efficiency and stability.”
The introduction of artificial intelligence (AI) in the manufacturing and shipbuilding sectors, where Korea has a global advantage, has been emphasized. It is a suggestion that we must maintain global competitiveness in the AI era by introducing and optimizing AI technology. At the manufacturing AI conference 'AIM with POSTECH 2024' held at KINTEX 2nd Exhibition Hall on the 24th, Jaesik Choi, CEO of INEEJI (KAIST professor), said, "47% of the 145 lighthouse factories around the world are using AI to increase production, stability, and efficiency through AI and digital transformation (DT)." He said, "We must introduce AI to manufacturing and shipbuilding, which are Korea's leading sectors, to secure global competitiveness."
He also emphasized the optimization of AI-based autonomous operations in the manufacturing sector and securing global competitiveness. “Through AI and digital transformation (DT), we will go beyond simple monitoring to prediction, control, and optimization,” he said. “AI technology can innovate the overall operating structure of the manufacturing industry and maximize operational efficiency.”
On this day, CEO Choi mentioned the case of SK PIC Global, explaining that by optimizing the driving route through an AI model, PO production increased by more than 2 tons per day and annual costs were reduced by 400 million won without additional facility investment.
INEEJI is a company that develops AI solutions used in manufacturing. It focuses on AI-based process optimization and autonomous manufacturing system development. It focuses on minimizing process stability, efficiency, and environmental impact through AI in energy-intensive industries such as steel, petrochemicals, and cement.
Recently, INEEJI is developing a foundation model that can predict using time series data. This model can be expanded into a service that predicts demand by collecting external and internal data in the manufacturing field. This model provides AI that can be explained as a demand prediction service, and if it is applied by entering related data such as demand, raw materials, exchange rates, financial indices, traffic, weather, and consumer behavior in addition to manufacturing, it can predict key indicators in an expanded manner. CEO Choi explained, “If existing AI systems were limited to simple analysis, the time series foundation model can predict the future and automatically respond to situations.”
