알림 목록

Analysis Area: Ulsan Metropolitan City

Core Area: Ulsan Dong-gu, Mipo National Industrial Complex, Onsan National Industrial Complex, Ulsan Port, and Shipbuilding and Marine Equipment Industry Zone

Key Industries: High Value-Added Ships, Eco-friendly Ships, Autonomous Ships, Smart Yards, Shipbuilding Equipment, Offshore Plants

Analysis Agenda: Is the recovery in shipbuilding orders and production leading to a transformation in AI-based design, production, quality, safety, and autonomous navigation, as well as the productivity of partner companies?

Golden Time Type: Orderbook Opportunity + Autonomous Shipyard Diffusion Risk

Version: Regional AX Golden Time Intelligence v3.2 Enhancement Standard

Reference Date: 2026.09.06
 

The South Korean shipbuilding industry secured 10,958,000 CGT in orders and built 11,460,000 CGT in 2024, achieving an order backlog of 37,999,000 CGT by the end of the year. Domestic shipyards, including HD Hyundai Heavy Industries in Ulsan, have entered a phase of production expansion based on a workload equivalent to more than three years' worth of work. However, the volume of orders in 2024 was lower than the volume of construction, and the workload growth ratio—calculated by dividing orders by construction volume— declined to 0.956 . There is no evidence that the current boom will automatically translate into long-term competitiveness. If productivity does not improve while the existing order backlog transitions into revenue and deliveries between 2026 and 2028, issues regarding labor shortages, delivery times, and costs will intensify simultaneously. While  the Ulsan shipbuilding industry has overcome the order crisis, it is not yet at the stage where it can be judged to have escaped the structural crisis of its production system. ( Data from the Korea Shipbuilding & Offshore Engineering Association )

HD Hyundai has set a goal to establish a "Future of Shipyard" (FOS) by 2030, aiming for a 30% increase in productivity and a 30% reduction in construction time . Phase 1, the "Visible Shipyard," was completed in December 2023, followed by Phase 2, which involves data connectivity and predictive optimization by 2026, and Phase 3, which aims to build an intelligent, autonomous shipyard by 2030. While the digital visualization of the shipyard has begun, the percentage of processes where AI has actually taken over planning, decision-making, and work control, as well as current productivity improvements, have not been publicly disclosed. If the achievements of Phase 2 (2026–2028) remain merely targets and pilot cases, the autonomous operation target for 2030 will remain a vision on the timeline.  The Ulsan AX shipyard has passed the digital visualization phase, but the performance of the autonomous optimization phase is still under verification. ( HD Korea Shipbuilding & Marine Engineering )

In Ulsan, a Performance Demonstration Center for the Autonomous Ship Technology Development Project was established with a total budget of 160.3 billion won from 2020 to 2025, and the construction of an AI Ship Demonstration Support Center worth 38.2 billion won in the Ulsan Mipo National Industrial Complex in Dong-gu has been confirmed for 2026. Regional infrastructure for verifying autonomous ships and AI/digital twin-based manufacturing is expanding. However, public evidence regarding the extent to which the technology from the demonstration centers has contributed to contracts, revenue, productivity, and international certifications for Ulsan shipyards and equipment manufacturers is limited. If centers, equipment, and projects increase over the next two to three years but corporate application rates remain low, Ulsan will remain merely a region for technology demonstration, and business value will accrue to external companies.  Currently, the "Golden Time" is Orderbook Opportunity + Autonomous Shipyard Diffusion Risk. ( Ministry of Oceans and Fisheries , UNIST )

The Ulsan shipbuilding industry features a supply chain structure centered around HD Hyundai Heavy Industries and HD Hyundai Mipo, connecting large shipyards, in-house and external subcontractors, and equipment, design, and maintenance firms. Since ships are made-to-order products that combine millions of parts and multiple processes over extended periods, delays in design changes, material procurement, block fabrication, outfitting, painting, and sea trials accumulate within the overall delivery timeframe. The industry has shifted from relying on a single AI model at a large shipyard to a structure where productivity is determined by the interconnectedness of schedule, quality, and material data across the entire supply chain. However, there is a lack of data standards between prime contractors and subcontractors, real-time linkage rates, and evidence regarding the causes of delays by process. If optimization occurs only within the prime contractors' internal systems between 2026 and 2028 while subcontractor data is delayed, the overall construction period will be tied to partial optimization. While  the Ulsan shipbuilding industry has achieved corporate agglomeration, it remains incomplete as a data-driven industrial ecosystem.

At the end of 2024, the number of employees in the domestic shipbuilding industry stood at 125,636, a recovery from 93,133 in 2021, but still approximately 78,000 fewer than the 203,441 recorded in 2014. Among the 100,340 skilled workers, the ratio of prime contractors to subcontractors was 26 to 74 , while administrative and technical personnel accounted for 25,296. This confirms a structure where production recovery relies heavily on skilled workers from a large number of subcontractors. There is no evidence demonstrating how process automation and the transition to AI alter the working conditions and skill levels of not only prime contractor technical personnel but also subcontracted production workers. If labor shortages are compensated for for the next two to three years solely through long working hours and foreign labor, the capacity to convert backlogs into production will be limited.  The current bottleneck for Ulsan's shipbuilding industry is not a lack of work, but a simultaneous shortage of both skill and productivity.

In 2024, South Korea's shipbuilding exports reached $25.636 billion, an increase from $18.178 billion in 2022, with gas carriers accounting for 60.7% of the order backlog. While export competitiveness centered on high-value-added gas carriers was maintained, the concentration on specific ship types also expanded. The product portfolio capable of absorbing the risks associated with over-reliance on gas carriers remains limited in the event of changes in eco-friendly regulations, the energy market, and ordering cycles. If the Chinese shipbuilding industry catches up with high-value-added ship types and smart vessels between 2026 and 2028, it will be difficult to defend against price and delivery competition relying solely on current ship type dominance. While  the competitiveness of Ulsan's shipbuilding industry is strong in high-value-added ship types, its ability to diversify in the next technology cycle remains uncertain.

The government invested approximately 260 billion won in the development of cutting-edge shipbuilding technologies in 2025, allocating 170 billion won to eco-friendly vessels, 70 billion won to the digital transformation of construction processes, and 20 billion won to autonomous vessels. In 2026, related investment was expanded by 23% to 320 billion won, with concentrated support provided for AI shipyards, AI vessels, eco-friendly vessels, and technologies for small and medium-sized shipbuilders. This structure expands the central axis of national R&D from ship product technology to production systems and AI. However, the amount of total national investment attributed to Ulsan companies is not separated from productivity and sales performance by project. If budget expansion over the next two to three years is cited as regional transformation achievement, the actual changes in Ulsan shipyards and their suppliers are overestimated. While  national investment in shipbuilding AX has expanded, the achievements attributed to the Ulsan industry are still subject to separate evaluation. ( Ministry of Trade, Industry and Energy , Republic of Korea Policy Briefing )

HD Hyundai’s FOS is being pursued by implementing a real-world shipyard in a virtual space using a digital twin and connecting design, production, and logistics information. The completion of Phase 1 visualization and the establishment of an integrated design and production platform serve as evidence that the physical foundation for data-driven operations has been formed. On the other hand, the 30% increase in productivity and the 30% reduction in construction time are targets for 2030, and current baselines and achievement rates by process are not confirmed in publicly available data. If the completion of Phase 2 by 2026 is judged solely by whether the system has been established, actual reductions in waiting times, rework, and process delays cannot be verified. Although FOS is a transition currently in progress, it cannot be judged as a "Smart Yard" that has already achieved its final outcomes.

Ulsan’s Autonomous Ship Performance Demonstration Center and AI Ship Demonstration Support Center provide infrastructure for research institutions, universities, and companies to test new technologies. While demonstration capabilities are concentrated in the region, full-cycle achievements—ranging from technology development to classification society certification, actual vessel application, shipyard adoption, and export contracts—are still being accumulated. A commercialization gap exists between the success of R&D and the success of market entry. If Ulsan’s technology lacks sufficient application track record during the period from 2026 to 2028, when international standards and classification rules are set in stone, leadership in demonstration will not translate into leadership in standards and the market.  Although Ulsan serves as a hub for autonomous navigation demonstration, its transition into an autonomous navigation industrial ecosystem remains uncertain.

The digital transformation of shipyards has shifted from the computerization of design drawings or the installation of individual robots to a direction that connects the entire shipyard's design, materials, personnel, processes, quality, and safety in real time. The structure is changing from one where workers report problems after the fact to one where AI predicts delays, collisions, and defects, and readjusts production plans. While Ulsan has entered this direction through FOS, the percentage of AI-recommended plans that actually lead to on-site decision-making and automated control is not disclosed. If a digital twin remains on a monitoring screen for two to three years, process time does not decrease, even if visibility increases.  The structural changes at the Ulsan Smart Yard are judged not by data visualization, but by the actual scope of AI decision-making authority.

Ships themselves are transforming from mechanical products into mobile systems that combine sensors, communications, software, remote navigation, and cybersecurity. Autonomous vessels integrate not only navigation control but also collision avoidance, engine status, cargo, energy usage, and land-based control. While Ulsan’s demonstration center has secured the foundation for radar, lidar, remote control, and real-sea verification, the scale of Ulsan equipment manufacturers entering the market as suppliers of core sensors, controls, and software remains unconfirmed. If platforms and core software fall under the ownership of external companies between 2026 and 2028, Ulsan will be unable to secure the data value of autonomous navigation even if it constructs ships.  The product competitiveness of Ulsan’s shipbuilding industry is shifting from hull construction to operational algorithms and data control.

IMO environmental regulations and the transition to decarbonization are expanding multiple technological pathways beyond LNG to include methanol, ammonia, hydrogen, electric propulsion, and carbon capture. Changes in fuel systems simultaneously transform engines, tanks, piping, safety, and operational optimization, while increasing the importance of AI-based energy management. Although Ulsan possesses the infrastructure for both eco-friendly ships and the energy industry, it lacks evidence that technologies, supply chains, and demonstration data have been integrated across different fuels. If investment remains fixed on a specific fuel technology for two to three years, the risk of failure increases due to changes in international regulations and order demand.  Ulsan's competitiveness in future ships is shifting from a single eco-friendly fuel to the ability to optimally manage multiple fuels using AI.

HD Hyundai’s FOS is currently being pursued with the goal of completing Phase 1 (virtual shipyard construction) in 2023, Phase 2 (connectivity and predictive optimization) in 2026, and Phase 3 (intelligent autonomous operation) in 2030. While phase schedules and final productivity targets exist, publicly available performance indicators showing changes in AI application rates, working hours, rework rates, waiting times, and delivery dates by process are limited. A measurement gap remains between the roadmap phases and on-site outcomes. If the results of Phase 2 (2026–2028) are managed solely by the number of solution implementations, there is a risk of overestimating readiness for autonomous operation. Although the policy and corporate execution levels of FOS are rated as high, the level of external verification for productivity and autonomy is assessed as medium-low.

HD Korea Shipbuilding & Marine Engineering (HDK) is pursuing the establishment of an integrated platform that links ship design and production into a single entity to realize a digital manufacturing environment for Ship Operations (FOS) by 2030. A structure in which design changes are immediately reflected in production, materials, and schedules serves as a foundation for reducing delay costs in the large-scale order-based manufacturing industry. However, the scope of the platform—which extends beyond the prime contractor to connect design, delivery, and quality data from subcontractors—and the rights to access this data remain unconfirmed. If subcontractors rely on files and manual reporting for two to three years, the real-time capability of the prime contractor's platform is halted at the supply chain boundary.  While design-production integration is underway, integration between the prime contractor and subcontractors remains unconfirmed. ( HDK )

Ulsan City has been pursuing a project since 2025 to train personnel in welding, electrical, painting, and insulation fields overseas and supply them to local shipyards and subcontractors. While a pathway has been established to address the shortage of short-term production workers, results linking the verification of foreign workers' skills, safety training, language proficiency, and long-term employment with their adaptation to AI work systems remain limited. A gap exists where workforce supply and the transition to production systems operate as separate policies. As foreign workers become concentrated in simple, repetitive, and high-risk processes between 2026 and 2028, investment in automation and the gap in working conditions will widen simultaneously.  Ulsan's workforce response is currently at the stage of securing supply volume, while the transition to a multinational and digital work system remains incomplete. ( Ulsan Metropolitan City )

In 2024, Korea secured orders for 10,958,000 CGT and maintained an order backlog of 37,999,000 CGT, preserving its position as a global shipbuilding powerhouse. However, China is expanding its market beyond bulk carriers and container ships to include LNG carriers and eco-friendly vessels, leveraging its large-scale production capacity and price competitiveness. Even if Korea maintains its advantage in high-value-added vessel types, the axis of competition will shift to production systems once the gap in construction time and costs narrows. Public evidence comparing the productivity, automation rates, and AI application rates of Korean and Chinese shipyards by process using the same standards is limited. If Ulsan fails to translate its technological superiority into a productivity advantage over the next two to three years, it will face pressure regarding delivery times and costs, even while maintaining high vessel prices.  While Ulsan holds a superior position in terms of vessel type, its competitiveness remains unassessable in international comparisons of smart yard productivity.

The workforce in Korea's shipbuilding industry plummeted from 203,441 in 2014 to 93,133 in 2021, before recovering to 125,636 in 2024. While the workforce is recovering, it has not fully restored the skilled foundation of the previous boom period. To counter China's massive workforce and Japan's standardization and automation, productivity per worker and process predictability—rather than the sheer number of employees—become the competitive conditions. Evidence comparing the labor productivity of Ulsan shipyards by process with that of overseas shipyards is not publicly available. If the order backlog for 2026–2028 is processed using existing labor-intensive methods, the labor shortage will translate into delivery risk. While  Ulsan has recovered its competitiveness in securing orders, it is currently in a phase where its international superiority in production capacity is being re-evaluated.

The government aims for the commercialization of fully autonomous ships by 2040 and has included autonomous navigation platforms and unmanned autonomous manufacturing processes among its top 10 flagship technologies. International competition has expanded from shipbuilding to ship operation software, data, standards, and certification. Although Ulsan possesses both a demonstration center and a major shipyard, it lacks evidence that simultaneously showcases international standard proposals, core patents, commercial operation data, and local corporate revenue. If demonstration data is not integrated into standards during the international rule formation phase of 2026–2028, ships may be built in Ulsan while operation platforms are supplied from external sources.  While Ulsan is the leading domestic hub for autonomous navigation demonstration, its leading position in global platforms remains unconfirmed.

In 2024, the South Korean shipbuilding industry constructed 11.46 million CGT, while new orders totaled 10.958 million CGT. Although the order backlog stood at a sufficient 37.999 million CGT, the volume of work newly secured that year was less than the volume processed. The current production boom is structurally mismatched with future order trends. If ship deliveries and new orders are not separated, revenue growth is mistaken for long-term growth. If the order backlog is rapidly depleted between 2026 and 2028 and new orders do not recover, investment in smart yards will also conflict with economic adjustments.  The current shipbuilding boom is not a matter of an absolute shortage of work, but rather a time asset that requires the production system to be restructured within the remaining time.

The domestic shipbuilding workforce increased by 32,503 from 93,133 in 2021 to 125,636 in 2024, but this is 77,805 fewer than in 2014. While production has recovered, the structure involves handling more processes without restoring past workforce levels. There is a lack of evidence separating which factor—workforce recovery or productivity improvement—explains the increase in shipbuilding volume. If the skill acquisition rate of new personnel fails to keep pace with the production speed of the order backlog over the next two to three years, both process delays and safety risks will increase.  The boom in Ulsan's shipbuilding industry depends more heavily on a shift in productivity than on workforce recovery.

The number of general skilled foreign workers (E-7-3) in the shipbuilding industry increased from 1,017 in 2022 to 13,297 in 2025 , while non-specialized employment (E-9) workers rose from 3,180 to 8,079. It was projected that the proportion of foreign workers among skilled workers in the shipbuilding industry would reach approximately 23% by 2026. This structure indicates a shift in foreign workers from serving as an auxiliary means to maintain production capacity to becoming core operational personnel. However, integrated evidence regarding language, proficiency, safety, and work instruction data is lacking. If multilingual work systems and safety standards are not established over the next two to three years, the expansion of the workforce will increase variability in quality and safety. While  the workforce structure of Ulsan's shipbuilding industry has already become multinationalized, the transition of the production management system to a multinational AX model remains unconfirmed.

Government investment in shipbuilding technology development is set to increase from approximately 260 billion won in 2025 to 320 billion won in 2026, connecting Ulsan with a 160.3 billion won autonomous navigation technology development base and a 38.2 billion won AI Ship Demonstration Support Center. While the scale of public investment and demonstration infrastructure has expanded, regional performance—translated into productivity, revenue, patents, standards, and export contracts—has not yet been verified. A gap exists between the scale of investment and its attribution to the industry. If only the number of equipment and projects increases over the next two to three years, it becomes difficult to determine the economic recovery of the demonstration infrastructure.  Ulsan’s Shipbuilding AX is not at a stage of insufficient investment, but rather at a stage where the attribution of investment results must be verified.

The biggest gap lies in the separation between the smartification of large shipyards and the AX of the entire supply chain . While HD Hyundai is pursuing FOS and an integrated design-production platform, 74% of the domestic skilled workforce is subcontracted, and many equipment suppliers have less data, manpower, and investment capacity than prime contractors. There is a lack of evidence that the prime contractor's process optimization is linked to the delivery, quality, and workforce management of partner companies. If the speed of AI within prime contractors outpaces the data preparation speed of partner companies between 2026 and 2028, bottlenecks in the entire supply chain will become more pronounced.  The success or failure of the Ulsan shipbuilding AX will be determined not by the most advanced shipyards, but by the partner processes that are connected the latest.

The second gap lies between targets and actual performance. While FOS proposed a 30% increase in productivity and a 30% reduction in construction time by 2030, it is difficult to consistently verify current productivity, process-specific targets, and annual achievement rates externally. The current measurement structure relies on the implementation of digital twins and the application of AI models to substitute for productivity outcomes. Even after the completion of Phase 2 in 2026, if changes in delays, rework, costs, and safety are not disclosed, it is impossible to make an interim assessment of the feasibility of the 2030 targets. Although the Ulsan Shipbuilding AX has a technology roadmap, it lacks an externally verifiable outcome roadmap.

The third gap lies in autonomous navigation demonstration and market ownership. Although Ulsan possesses the National Autonomous Ship Performance Demonstration Center and the New AI Ship Demonstration Support Center, the product certifications, shipyard adoptions, export contracts, and recurring revenue of demonstration companies are not disclosed in an integrated manner. The structure is such that R&D and commercialization are managed by separate institutions and performance indicators. Unless commercial operation data is accumulated during the two to three years it takes for international standards and classification rules to be established, securing facilities does not translate into securing a market advantage.  Ulsan's autonomous navigation gap lies not in the technology itself, but in the failure to identify buyers after the demonstration.

The fourth gap lies in the combination of people and automation. The shipbuilding industry is pursuing robot and AI automation while compensating for the shortage of skilled labor by expanding the foreign workforce and utilizing retired experts. However, there is a lack of evidence connecting which processes to automate, which skills to retain, and what roles new personnel will undertake after automation. If workforce and automation policies are pursued separately for two to three years, the industry will simultaneously face labor shortages and underutilized technology.  The labor shortage in Ulsan's shipbuilding industry is not a gap in the number of people, but a gap in the allocation of processes, skills, and automation.

Ulsan is home to a concentration of world-class large shipyards, Ulsan Port, UNIST, Ulsan Technopark, the Autonomous Ship Performance Demonstration Center, and the AI ​​Ship Demonstration Support Center. The physical infrastructure for developing and demonstrating Shipbuilding AX ranks among the best in the country. However, data interoperability between facilities, the rate of shared use by companies, and contract outcomes after demonstration are not disclosed. A gap remains between facility concentration and the integration of industrial operating systems. If the equipment and projects of each institution are operated separately from 2026 to 2028, the effects of concentration will not accumulate as a shared asset.  The infrastructure readiness of Ulsan Shipbuilding AX is assessed as high, while the readiness for integrated operation is assessed as medium-low.

In 2024, the domestic shipbuilding industry consisted of 100,340 skilled workers and 25,296 administrative and technical personnel, with 74% of the skilled workforce belonging to subcontractors. While the quantity of the workforce has recovered, the levels of proficiency and retraining by job function to operate AI, robots, and digital twins have not been verified. There is a lack of a competency framework that connects existing skilled workers, foreign workers, and digital specialists into a single work team. If technology adoption outpaces the transition of workers' skills over a period of two to three years, the utilization rate in the field will remain low even if the system is installed. The readiness for  workforce supply is assessed as medium, while the readiness for AX proficiency is assessed as medium-low.

FOS and the design-production integrated platform serve as the foundation for connecting internal data at large shipyards. However, the data formats, access rights, and cybersecurity standards regarding equipment, processes, quality, and delivery from partner companies are not disclosed in an integrated manner. This leaves open the possibility that shipbuilding industry data will be fragmented into assets specific to individual companies and projects. If the structure of individual platforms remains fixed for two to three years, small and medium-sized enterprises (SMEs) will have to respond redundantly to multiple prime contractor systems.  The readiness for prime contractor data is assessed as medium-high, while the readiness for shared supply chain data is assessed as low.

HD Hyundai’s Phase 1 FOS and the establishment of an integrated design-production platform serve as evidence that the operational methods of large shipyards have shifted to a data-centric model. However, it has not been confirmed whether the productivity, defect rates, delivery times, and costs of partner companies have improved alongside this. There is an attribution gap between the digital achievements of large corporations and the on-site performance of local companies. If prime contractors’ delivery requirements become more sophisticated between 2026 and 2028 without matching support in terms of equipment and personnel from partner companies, data will function as a management tool that pressures them. While  smart yards have begun to move to prime contractor sites, it has not yet been determined that they have spread to the regional supply chain.

Ulsan operates a local training system to supply foreign workers to the shipbuilding industry in the fields of welding, electrical work, painting, and insulation. While a structure has been established to respond to companies' immediate labor demands, there is a lack of integrated evidence tracking work quality, safety accidents, tenure, and skill advancement after training. A gap remains between hiring and on-site integration. If only short-term recruitment is repeated for two to three years, skills will not accumulate within the companies and the region.  Although foreign workers are moving to the field, it is premature to conclude that they have been converted into long-term skilled assets for the industry.

AI and robots have the potential to automate dangerous welding, painting, transportation, and inspection processes, thereby enhancing worker safety. However, publicly available data only confirms, to a limited extent, the robot application rates by process, reductions in musculoskeletal burden, accidents, and risk exposure, as well as changes in workers' wages and job duties. There is a measurement gap between technology adoption and labor outcomes. If only productivity is managed and safety and job transitions are not measured between 2026 and 2028, workers may perceive AX not as beneficiaries but as targets for surveillance and workforce reduction risks. Whether  the shipbuilding AX has been passed on to workers is determined not by the number of equipment, but by the reduction in hazardous tasks and the increase in the value of skills.

Ulsan's Dong-gu district is concentrated with large shipyards, subcontractors, the Autonomous Ship Performance Demonstration Center, and the New AI Ship Demonstration Support Center. While there is strong spatial proximity between research, production, and demonstration, a transaction network connecting AI and software companies with shipbuilding equipment manufacturers across Ulsan has not been identified. A gap exists between the industrial agglomeration of Dong-gu and the city's overall AX ecosystem. If achievements are concentrated only around large shipyards for the next two to three years, Ulsan's AI city strategy and shipbuilding AX will become entrenched as separate zones.  The spatial strength of Ulsan's shipbuilding AX lies in its concentration in Dong-gu, and the spatial risks are also skewed towards this district.

The Ulsan Mipo and Onsan National Industrial Complexes and Ulsan Port possess the infrastructure to connect the shipbuilding, machinery, chemical, and logistics industries. While the conditions exist to combine eco-friendly ship fuel, equipment, and port demonstrations, there is a lack of evidence that data and demonstration projects across the industrial complexes are connected into a single operating system. A gap remains between the physical proximity of industries and technological convergence. If the AX projects for shipbuilding, energy, and ports are pursued as individual initiatives between 2026 and 2028, Ulsan’s strength in complex industries will be limited to mere location. Although  Ulsan possesses the conditions to be an urban testbed for eco-friendly and autonomous ships, it remains incomplete as an integrated demonstration zone.

Shipbuilding partner companies are connected to equipment, processing, and logistics firms not only in Ulsan but also in Busan and Gyeongnam. Focusing solely on companies within administrative boundaries makes it impossible to detect delays and data disconnections in the actual supply chain. Evidence linking process, delivery, and quality data across the Bu-Ul-Gyeongnam shipbuilding supply chain is unavailable. If an Ulsan-only platform remains fixed for two to three years, the wider regional supply chain undergoes repeated separate input and manual conversion.  The actual spatial unit of the Ulsan Shipbuilding AX is not Ulsan City, but the Bu-Ul-Gyeongnam shipbuilding supply chain.

HD Hyundai FOS entered the second phase of connectivity and predictive optimization in 2026 and aims to complete an intelligent, autonomous shipyard by 2030. Currently, we are at a point where data structures, AI models, and on-site decision-making methods are beginning to solidify into a long-term operating system. If data from partner companies and workers is excluded from the initial design, integration costs will skyrocket thereafter. The period from 2026 to 2028 is the final intermediate phase where platform construction and supply chain expansion can be verified simultaneously. The Golden Time is not the completion date of 2030, but the present moment when the second-phase operational structure becomes fixed.

Domestic shipyards held an order backlog of 37,999,000 CGT at the end of 2024, but the volume of orders received that year was less than the volume of shipbuilding. The current period, characterized by active production, is the most favorable time to simultaneously secure investment costs and on-site data. Once the order backlog shrinks, the investment capacity of partner companies and workers decreases first. If actual process data is not secured for two to three years, it will be difficult to restart pilot projects during a recession.  The current order backlog serves as both a revenue asset and a limited data asset for training the shipbuilding AX.

The Autonomous Ship Technology Development Project completed Phase 1 in 2025, and the construction of the AI ​​Ship Demonstration Support Center will follow starting in 2026. As the conclusion of R&D coincides with the launch of the new demonstration infrastructure, a window has opened to convert technology into corporate products and contracts. If existing achievements are not connected to the follow-up center, data, equipment, and personnel will become fragmented again. If the period of 2026–2028, when international standards and commercial markets are formed, is missed, the market value of the leadership in demonstration will decline.  The Golden Time for autonomous navigation in Ulsan is not the construction of additional centers, but the period during which the achievements of the two projects are connected to commercialization.

12-1. Golden Time Application Case in Basic Local Governments ① — Dong-gu, Ulsan

Ulsan Dong-gu is one of the largest shipbuilding sites in Korea, where HD Hyundai Heavy Industries, HD Hyundai Mipo, numerous partner companies, and demonstration bases for autonomous navigation and AI ships are concentrated. While industrial agglomeration is strong, a large portion of the skilled workforce belongs to the cooperative structure, and the proportion of foreign labor has also increased. However, there is a lack of evidence connecting equipment, quality, and safety data from prime contractors, partner companies, and domestic and foreign workers. If prime contractors' smart yards are not connected to local cooperative processes between 2026 and 2028, Dong-gu will become entrenched as a region where world-class shipyards coexist with digitally vulnerable partner companies.  Dong-gu's Golden Time is the period during which the AI ​​of large shipyards is translated into productivity for the local shipbuilding ecosystem.

12-2. Golden Time Metropolitan Industrial Zone Application Case ② — Busan-Ulsan-Gyeongnam Shipbuilding Supply Chain

The Ulsan shipyard forms a single production network with equipment, processing, design, and logistics companies in Busan and Gyeongnam. The delivery time and quality of a single vessel depend on the data and work speed of suppliers located outside the administrative district. However, a common material code, quality history, and delivery forecasting system that runs through Ulsan, Busan, and Gyeongnam has not been identified. If regional AX businesses remain independent and fixed for two to three years, companies will redundantly respond to multiple platforms and standards.  The Golden Time for the Bu-Ul-Gyeong shipbuilding AX is not the period for expanding regional businesses, but the period during which the entire supply chain is viewed on a single timeline.

In 2024, the workforce in the domestic shipbuilding industry was approximately 78,000 fewer than in 2014, with 74% of skilled workers belonging to a subcontracting structure. While the aging of skilled workers and a shortage of new personnel are occurring simultaneously, work knowledge remains with individuals and work teams. If this knowledge is not converted into data, standard procedures, or AI models, it will be lost upon retirement or relocation. If the workforce structure changes further in two to three years, it will be difficult to reconstruct the reasons behind past work decisions.  What is currently being overlooked is not the number of workers, but the tacit skill data accumulated in Ulsan's shipbuilding industry.

Ulsan has secured the Autonomous Ship Demonstration Center and the AI ​​Ship Demonstration Support Center. However, if the demonstration results do not lead to international standards, classification society certifications, commercial products, or export contracts, the market value of the technology accrues to other platform companies. The structure is such that even if shipbuilding remains in Ulsan, operational data and software revenue remain outside the city. If commercial operation history and customers are not secured between 2026 and 2028, differentiation from latecomer platforms will weaken.  What is being missed now is not autonomous navigation technology, but control over ship lifecycle data.

If shipyard platforms become fixed around prime contractors, partner companies remain merely data providers, and AI models, analysis, and transaction value are concentrated among prime contractors and external solution providers. The right to use data, which would allow partner companies to accumulate their own productivity and technology, remains unconfirmed. If platform contracts and data rights remain fixed for two to three years, it becomes difficult to reverse the value distribution structure of the supply chain.  What Ulsan could miss is not the participation of SMEs in AX, but rather their bargaining power in the shipbuilding data economy.

HD Hyundai’s FOS, the Design and Production Integrated Platform, the Autonomous Ship Performance Demonstration Center, and the AI ​​Ship Demonstration Support Center are all located in the same area. The physical conditions to connect data from shipyards, vessels, and actual sea areas into a single industrial cycle have already been established. However, currently, the data is separated by company, institution, and business sector. When the Design-Production-Operation-Maintenance Evidence is connected between 2026 and 2028, Ulsan will transform from a region that builds ships into a region that learns about the entire lifecycle of a vessel.  The greatest asset currently available is not new facilities, but continuous data covering the ship lifecycle.

The Ulsan shipyard simultaneously hosts large-scale actual production processes, a multinational workforce, and diverse skill levels. These provide on-site conditions suitable for verifying robots, AI, and multilingual safety systems. By linking data on working hours, quality, risk, and skill levels for each process, it is possible to determine the priorities for automation and the tasks to retain for humans. Once this evidence is established within two to three years, the labor shortage will transform from a mere recruitment issue into a basis for redesigning the production system.  The structural asset currently available is not labor replacement, but the optimal work placement of humans and AI.

The order backlog in 2024 and the production expansion in 2026 provide the volume of data necessary for AI models to learn various ship types, processes, and operating conditions. During a recession, processes and workloads decrease, which also reduces the scope of actual on-site verification. By securing production data from 2026 to 2028 as a baseline, the productivity, delivery, and safety effects of FOS can be objectively compared.  The time assets currently available represent an opportunity to verify the effectiveness of the smart yard against actual boom production levels.

The scope of AX response is not merely about adding AI and robot equipment to shipyards. It is a  structure that connects the order, design, materials, production, quality, delivery, operation, and maintenance processes into a single Evidence Chain, and verifies at runtime whether the prime contractor's productivity improvement is attributed to the performance of partner companies, workers, and autonomous products.

The first Runtime establishes representative vessel types and processes to which FOS Phase 2 is applied as the reference cohort. It compares work time, waiting time, rework, defects, safety, delivery time, and cost before and after application using the same criteria, and includes changes in partner companies and domestic and foreign workers. The autonomous navigation sector tracks progress from technology demonstration to classification society certification, vessel application, and export contracts.

The condition for failure is not the absence of AI technology. While digital screens and demonstration centers at major shipyards are increasing, overall construction periods are not decreasing, the productivity and profitability of partner companies are not improving, and the value of autonomous navigation platforms and ship data remains outside of Ulsan.

Orderbook Opportunity + Autonomous Shipyard Diffusion Risk

Ulsan Shipbuilding AX did not stop at mere declarations. The future shipyard roadmap, design and production integrated platform, autonomous navigation demonstration center, and AI ship demonstration base have entered the actual construction phase.

The current risk is not a lack of technology or facilities. It lies in the lack of evidence that the changes at large shipyards have spread to partner companies, workers, equipment suppliers, and the commercialization of autonomous navigation.

The period from 2026 to 2028 is a Golden Time where the production of existing order backlogs, Phase 2 of FOS, the establishment of an AI ship demonstration center, and the formation of international autonomous navigation rules overlap. Once this period passes, gaps in platform, data authority, and supply chains will become fixed, and training data from the boom period will also be lost.

Final judgment: Orders and industrial base are high, large shipyard AX implementation is medium-high, supply chain expansion is medium-low, autonomous navigation demonstration is medium-high, commercialization and data ownership are medium-low, and time risk is high.

The verification sequence is Order → Design → Production Planning → Process Execution → Quality and Safety → Delivery → Operation of the actual vessel → Recurring Order . If any one of the number of AI models, robots, or demonstration projects increases, it will not be judged as a Shipbuilding AX achievement.

Runtime Chain

Orderbook Evidence → AI Shipyard Execution → Supplier Diffusion → Productivity Outcome → Autonomous Vessel Commercialization → Regional Value Retention

Structural insights remaining from this analysis

The structural gap in Ulsan's shipbuilding industry does not lie in a lack of AI technology. Ulsan possesses world-class shipyards, an FOS roadmap, an integrated design-production platform, an Autonomous Ship Performance Demonstration Center, and an AI Ship Demonstration Support Center. What is lacking is connected evidence demonstrating how AI at major shipyards has transformed the construction time of a single vessel and the productivity of the entire supply chain .

Shipyards record order, design, and production data; partner companies record delivery, quality, and personnel; and demonstration centers accumulate sensor, navigation, and operation data. As long as this data remains separated, bottlenecks in collaborative processes persist even if large shipyards become smarter, and even if autonomous navigation technology is proven, the data value of the ship's lifecycle is attributed to a separate platform.

The shipbuilding industry is not a factory that repeatedly produces identical products, but a massive project industry where designs and processes change for each vessel. Therefore, competitiveness is determined not by the automation rate of a single process, but by the ability to recalculate changes in design, materials, manpower, quality, and delivery times on an overall vessel basis. If partial automation fails to reduce the overall construction period, technological investment does not translate into productivity.

Golden Time Thesis — The Golden Time for Ulsan’s shipbuilding industry from 2026 to 2028 is not a period for installing more AI equipment, but rather a period to transform prime contractors, subcontractors, workers, and vessels into a single autonomous Evidence Chain by utilizing actual production data from the remaining order backlog. Once this connection is formed, Ulsan will shift from a city that builds ships to a city that learns about the design, production, and operation of ships. If this connection is not established, the shipbuilding boom will end, leaving only the facilities and goals of the smart yard.

Major Evidence Sources

• Ministry of Trade, Industry and Energy – 320 billion KRW investment in K-Shipbuilding super-gap technology development by 2026
• Republic of Korea Policy Briefing—2025 K-Shipbuilding Super-Gap Technology Development
• K-Shipbuilding Next-Generation Leadership Strategy
• HD Korea Shipbuilding & Offshore Engineering—Integrated Ship Design and Production Platform
• HD Korea Shipbuilding & Marine Engineering—2025 Sustainability Report
• Ministry of Oceans and Fisheries—Ulsan Autonomous Ship Performance Demonstration Center
• UNIST—38.2 billion KRW AI Ship Demonstration Support Center
• Ulsan Metropolitan City—Fostering the shipbuilding industry
• Current Status and Challenges of the Korean Shipbuilding Industry
• Ministry of Trade, Industry and Energy—Completion of Autonomous Ship Performance Demonstration Center