South Korea's domestic manufacturing sector is facing a new challenge: the "severance of experience." As skilled workers leave the shop floor, they take with them not only operational techniques but also the cumulative experience and decision making processes required to interpret equipment anomalies and resolve problems. Consequently, the future competitiveness of manufacturing AI will depend on how effectively shop floor experience is accumulated and utilized, rather than on the AI models themselves.
According to industry sources on the 1st, domestic manufacturing is simultaneously experiencing a decline in the employed population and an aging workforce structure. Last year, the number of employed individuals in manufacturing stood at 4,382,000, down by 73,000 from the previous year. Notably, the number of employed persons aged 15 to 29 dropped by 61,000, whereas those aged 60 and older increased by 54,000.
The primary reason these demographic shifts translate into a critical manufacturing challenge is tacit knowledge. Tacit knowledge refers to insights formed through physical experience, sensory perception, intuition, and situational judgment. On the factory floor, when observing identical equipment, veteran technicians narrow down root causes and determine the sequence of corrective actions by factoring in vibration, sound, temperature changes, product states, operating conditions, and past maintenance histories. Such experience and judgment are difficult to fully transfer through standard operating procedures or training manuals alone.
The core issue is that enterprises are struggling to sufficiently capture and store this manufacturing tacit knowledge. As a result, converting manufacturing tacit knowledge into digital assets has emerged as a pressingly vital task.
In response, the South Korean government allocated 12.8 billion KRW this year to support the demonstration and expansion of industrial AI solutions, while newly budgeting 6.0 billion KRW for the development of industrial AI agent technologies that autonomously resolve shop floor issues. This expands the policy scope beyond isolated AI demonstrations toward applying technologies to actual manufacturing processes to accumulate verifiable performance. However, deploying AI directly to field sites does not instantly guarantee technology transfer. A structured architecture is required to link equipment and process data with operator judgments and action outcomes, allowing AI to learn from and apply past experiences.
To address this, manufacturing AI specialist ONEPREDICT is drawing significant industry attention by connecting shop floor data with decision making processes through its manufacturing data platform 'cyclone' and industrial AI operation platform 'pdx'. 'cyclone' links equipment, process, production, quality, and maintenance data based on equipment, time, product, and recipe criteria. By grouping load factors, operating conditions, quality shifts, and historical maintenance logs at the exact moment vibration increases into a single event, it constructs the contextual data required for AI to analyze site conditions at the moment of judgment.
'pdx' then translates the data context structured by 'cyclone' into actionable workflows. When AI presents anomaly types and root cause candidates, supervisors decide on inspection targets and action plans, while technicians record the execution details, such as inspections and component replacements. Subsequently, the system verifies through data whether equipment conditions and product quality have normalized, recording the efficacy of the corrective action. While 'cyclone' reconstructs the situation at the moment of judgment, 'pdx' serves to capture the interpretation and response process as an integrated operational history.
When these two platforms are interconnected, shop floor issues transition from one off job records into organizational experience that informs future decision making. Structuring manufacturing tacit knowledge previously dependent on individual memories and habits into data, AI, and workflow processes converts it into an asset that enterprises continuously own and learn from. This 'Manufacturing Operational Intelligence' lies at the core of ONEPREDICT's vision for the 'AI Native Factory'. Rather than simply adding isolated AI tools to individual processes, it establishes a structure where decisions and execution outcomes generated on the floor are continuously re accumulated as organizational knowledge.
An official from ONEPREDICT stated, "By leveraging 'cyclone' and 'pdx' to connect manufacturing data, industrial AI, and field operations, we are advancing toward an 'AI Native Factory' where problem solving experience accumulates into organizational knowledge. Transforming the tacit knowledge of skilled workers from personal experience into manufacturing operational intelligence that the organization can continuously utilize represents ONEPREDICT's vision for the factory of the future."
Source: Asia Today (https://www.asiatoday.co.kr )
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South Korea's domestic manufacturing sector is facing a new challenge: the "severance of experience." As skilled workers leave the shop floor, they take with them not only operational techniques but also the cumulative experience and decision making processes required to interpret equipment anomalies and resolve problems. Consequently, the future competitiveness of manufacturing AI will depend on how effectively shop floor experience is accumulated and utilized, rather than on the AI models themselves.
According to industry sources on the 1st, domestic manufacturing is simultaneously experiencing a decline in the employed population and an aging workforce structure. Last year, the number of employed individuals in manufacturing stood at 4,382,000, down by 73,000 from the previous year. Notably, the number of employed persons aged 15 to 29 dropped by 61,000, whereas those aged 60 and older increased by 54,000.
The primary reason these demographic shifts translate into a critical manufacturing challenge is tacit knowledge. Tacit knowledge refers to insights formed through physical experience, sensory perception, intuition, and situational judgment. On the factory floor, when observing identical equipment, veteran technicians narrow down root causes and determine the sequence of corrective actions by factoring in vibration, sound, temperature changes, product states, operating conditions, and past maintenance histories. Such experience and judgment are difficult to fully transfer through standard operating procedures or training manuals alone.
The core issue is that enterprises are struggling to sufficiently capture and store this manufacturing tacit knowledge. As a result, converting manufacturing tacit knowledge into digital assets has emerged as a pressingly vital task.
In response, the South Korean government allocated 12.8 billion KRW this year to support the demonstration and expansion of industrial AI solutions, while newly budgeting 6.0 billion KRW for the development of industrial AI agent technologies that autonomously resolve shop floor issues. This expands the policy scope beyond isolated AI demonstrations toward applying technologies to actual manufacturing processes to accumulate verifiable performance. However, deploying AI directly to field sites does not instantly guarantee technology transfer. A structured architecture is required to link equipment and process data with operator judgments and action outcomes, allowing AI to learn from and apply past experiences.
To address this, manufacturing AI specialist ONEPREDICT is drawing significant industry attention by connecting shop floor data with decision making processes through its manufacturing data platform 'cyclone' and industrial AI operation platform 'pdx'. 'cyclone' links equipment, process, production, quality, and maintenance data based on equipment, time, product, and recipe criteria. By grouping load factors, operating conditions, quality shifts, and historical maintenance logs at the exact moment vibration increases into a single event, it constructs the contextual data required for AI to analyze site conditions at the moment of judgment.
'pdx' then translates the data context structured by 'cyclone' into actionable workflows. When AI presents anomaly types and root cause candidates, supervisors decide on inspection targets and action plans, while technicians record the execution details, such as inspections and component replacements. Subsequently, the system verifies through data whether equipment conditions and product quality have normalized, recording the efficacy of the corrective action. While 'cyclone' reconstructs the situation at the moment of judgment, 'pdx' serves to capture the interpretation and response process as an integrated operational history.
When these two platforms are interconnected, shop floor issues transition from one off job records into organizational experience that informs future decision making. Structuring manufacturing tacit knowledge previously dependent on individual memories and habits into data, AI, and workflow processes converts it into an asset that enterprises continuously own and learn from. This 'Manufacturing Operational Intelligence' lies at the core of ONEPREDICT's vision for the 'AI Native Factory'. Rather than simply adding isolated AI tools to individual processes, it establishes a structure where decisions and execution outcomes generated on the floor are continuously re accumulated as organizational knowledge.
An official from ONEPREDICT stated, "By leveraging 'cyclone' and 'pdx' to connect manufacturing data, industrial AI, and field operations, we are advancing toward an 'AI Native Factory' where problem solving experience accumulates into organizational knowledge. Transforming the tacit knowledge of skilled workers from personal experience into manufacturing operational intelligence that the organization can continuously utilize represents ONEPREDICT's vision for the factory of the future."
Source: Asia Today (https://www.asiatoday.co.kr )