Automation was once a choice made to increase productivity. However, as labor shortages and rising labor costs overlap, automation is now becoming a condition for maintaining production. Robots and automation systems are emerging not as technologies that push people out, but as a new foundation for stabilizing increasingly fragile industrial sites.

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Industrial automation is gaining speed again. Automation has long been one of the representative terms used to describe the future of manufacturing. Stories about robots entering factories, production lines becoming digitalized, and machines taking over work once done by human hands are no longer unfamiliar. Yet the current trend is somewhat different from the past. If automation in earlier periods was the language of productivity competition, aimed at making things faster and cheaper, today’s automation is becoming closer to the language of survival, a way to keep production from stopping in an era when it is difficult to find workers.

Labor shortages are no longer a problem limited to specific industries or regions. Complaints about the difficulty of finding workers are spreading across manufacturing, logistics, construction, agriculture, food service, care work, and retail sites. Manufacturing, in particular, is an industry that requires a steady input of labor to maintain production. When one person is absent, it does not simply mean that one position is empty. The pace of the process changes, the connection between preceding and following tasks becomes unstable, and the subtle quality control that depended on the judgment of skilled workers weakens. The absence of a single worker can easily slow the flow of an entire production line.

The problem is that hiring new workers is no longer as easy as it once was. The younger population is shrinking, fewer people prefer production jobs, and there is a growing tendency to avoid shift work, repetitive labor, and hazardous working environments. Companies in the field post job openings, but applicants are scarce. Even when they manage to hire new workers, many do not stay for long. It takes time to teach and train new employees, but when workers leave before that time has passed, companies have to repeat the same training again and again. In that process, costs rise, productivity falls, and the fatigue of existing workers increases.

Production gaps place very real pressure on companies. There are orders, but not enough people to make the products. There are facilities, but not enough workers to run them. Deadlines are fixed, but it becomes difficult to maintain the pace of production. In manufacturing, delivery deadlines are not merely schedules; they are a matter of trust. Once delivery becomes unstable, customers begin looking for other suppliers, and a company can lose ground in a competition of trust that is even more important than price competitiveness. For this reason, labor shortages grow beyond a simple hiring problem and become an issue that affects supply chain stability and corporate survival.

In this situation, automation emerges not as an option but as an alternative. Automation is not a device for eliminating people altogether, but a device for reducing the production instability caused by human labor gaps. Robots repeat consistent motions, automation systems maintain processes at a fixed pace, and sensors and control systems monitor production conditions in real time. In the sense that a certain level of production flow can be maintained even when human hands are in short supply, automation serves as a safety valve in the age of labor shortages.

The need for automation grows especially stronger in tasks that are repetitive and predictable. Moving, assembling, packaging, inspecting, and loading parts have long depended on human hands, but they are also areas where labor shortages can most easily lead to production disruptions. When these tasks are automated, people can focus on more complex judgment and management work. Automation does not simply replace people; it becomes a process of redistributing work between what people should do and what machines should handle.

As labor shortages intensify, companies cannot solve the problem merely by hiring more people. Demographic change does not reverse in the short term. A lack of skilled labor also cannot be resolved overnight. If so, companies must find ways to produce more reliably with the same workforce. Automation is one such answer. In an era when the number of workers cannot be expanded endlessly, companies respond by changing the production system itself. This is the first reason industrial automation is accelerating again.

Automation investment has always been tied to the question of cost. Introducing robots, changing production lines, installing equipment, and connecting software all require a significant amount of money. In the past, these initial costs were a major barrier to the spread of automation. Companies understood the benefits of automation but hesitated while calculating the payback period. In particular, companies engaged in high-mix, low-volume production, as well as small and medium-sized manufacturers, felt a heavy burden because automation systems would have to be adjusted again whenever production items changed.

However, as labor costs continue to rise, the calculation behind automation is changing. The cost of employing people does not end with wages alone. It includes recruitment costs, training costs, management costs, welfare costs, safety management costs, and retraining costs caused by turnover. When additional wages for night shifts, weekend work, hazardous duties, and securing skilled labor are added, the actual cost of human-centered production becomes even greater. The burden felt by companies is far broader than the numbers written on a wage statement.

Rising labor costs shorten the payback period for automation systems. In the past, the cost of introducing a single robot felt burdensome, but today the cost of continuously hiring and retaining workers for repetitive tasks can feel even heavier. Especially in labor-intensive processes, the economic value of automation increases as labor costs rise. Robots require high upfront investment, but after a certain period, recurring costs become lower. By contrast, human-centered methods continue to see increases in wages and management costs over time. This difference is changing the criteria by which companies judge automation investment.

Of course, automation does not immediately generate profits for every company. Introducing automation systems requires process analysis, standardization of production flows, and personnel capable of managing the equipment. Simply buying robots and placing them in a factory does not automatically increase productivity. Automation investment is not about buying machines; it is about redesigning the way work is done. Nevertheless, the reason companies are becoming more proactive about automation is that they can no longer bear the cost burden through the old method of continuously hiring and managing workers.

Rising labor costs are also pushing automation forward in the service sector. Kiosks in restaurants, unmanned ordering machines in cafes, self-checkout counters in stores, automated sorting systems in warehouses, and automated reception systems in hospitals all reflect the pressure of labor costs and labor shortages. At first, these technologies may look like tools for customer convenience, but in practice, they function as tools for maintaining operations with fewer workers. By reducing the need for workers to take orders, handle payments, and perform repetitive guidance tasks, the remaining staff can focus on more important areas such as cooking, customer service, and quality control.

For companies, automation is not simply a tool for reducing costs. Automation increases the predictability of costs. Human-centered operations can vary greatly in cost and productivity depending on labor supply conditions, wage increases, turnover rates, and training levels. By contrast, automation systems operate within a certain range, even though they require management and maintenance. Companies can predict production volume and costs more easily, and they can establish long-term plans with greater confidence. In an era of growing uncertainty, the ability to predict costs is as important as the ability to reduce them.

Rising labor costs are making companies’ decisions about automation investment more realistic. In the past, automation was a proactive investment for future competitiveness. Now it is becoming a response to withstand the current cost structure. In particular, once competitors begin lowering production costs and stabilizing quality through automation, companies that delay automation have no choice but to face pressure on both price and delivery. Automation is no longer just the choice of one company; it is becoming a competitive condition for entire industries.

One of the most difficult problems in industrial sites is the shortage of skilled labor. A shortage of people and a shortage of skilled workers are different problems. Some problems cannot be solved simply by hiring people. The process intuition that only long-time workers possess, changes in machine sounds, subtle differences in material conditions, signs that appear before defects occur, and small adjustments in work sequence are not easily documented. Quality in manufacturing sites is often maintained on the basis of such experienced workers’ accumulated know-how.

When skilled workers become scarce, quality risks grow. Even when the same equipment is used, results can vary depending on who handles it. Even when the same parts are assembled, the defect rate can change depending on the amount of force applied, the sense of positioning, and the balance of work speed. Especially in industries with strict quality standards, such as precision manufacturing, electronic components, automotive parts, batteries, biotechnology, and food processing, small errors can lead to major losses. One defect can lead to recalls, delivery delays, and a decline in brand trust.

The shortage of skilled labor is also connected to generational transition. When workers who have protected the field for a long time retire, the tacit knowledge they possess disappears with them. Companies create manuals and run training programs, but it is not easy to transfer every experience into documents. There are many judgments in the field that are difficult to explain in writing. Information acquired physically by skilled workers, such as temperature, sound, smell, vibration, and the feel of materials, disappears as personal experience unless it is converted into data. This is precisely why automation and digitalization are important.

Automation cannot completely replace the intuition of skilled workers. However, it can transfer part of that intuition into systems. Sensors continuously measure information such as temperature, pressure, vibration, position, speed, humidity, current, and noise. Vision systems check the appearance and dimensions of products, and artificial intelligence learns defect patterns. Predictive maintenance systems detect abnormal signals before equipment fails. As this data accumulates, quality management that once depended on the experience of specific individuals can be transformed into an organizational system.

The reason automation is important in reducing quality risks lies in repeatability and consistency. No matter how skilled people are, they are affected by fatigue, loss of concentration, working conditions, and working hours. When the same work is repeated for a long time, mistakes inevitably occur. By contrast, automation systems repeat the same movements under fixed conditions. Of course, equipment errors or program errors can occur, but if management is carried out properly, quality variation can be greatly reduced. Inspection automation, in particular, makes it possible to check tiny defects that people may easily miss according to consistent standards.

Automation does not make skilled workers unnecessary; it changes the content of skill. If past skill was concentrated in the sense of the hands and field experience, future skill will expand into the ability to understand equipment, read data, and improve processes. The greatest productivity emerges when people with field experience understand automation systems. More important than a person who knows how to operate robots is a person who understands the essence of the process and can place robots appropriately. Even in the age of automation, skill does not disappear. Its form changes.

Companies must prepare for this change. Before skilled workers retire, their experience must be transferred into data and manuals, and younger workers must be trained so that they can grow together with automation systems. Automation is not completed by the introduction of technology alone. Human experience must be embedded into systems, and data produced by systems must be reconnected to human judgment. The shortage of skilled labor pushes automation forward, but the success of automation also depends on a new kind of skilled workforce.

Robots are the most visible symbol of industrial automation. Multi-joint robots that move like human arms, collaborative robots that can work alongside people, autonomous mobile robots that move through logistics warehouses, picking robots that sort products, and vision robots combined with inspection equipment are rapidly spreading across many industrial sites. In the past, robots were mainly used in mass-production environments such as automobile factories. They were strong in repetitive and dangerous tasks such as welding, painting, and assembly. Recently, however, robots have become smaller and more flexible, and they are evolving in a direction that allows them to enter high-mix production environments as well.

Process stabilization is an important purpose of robot adoption. Companies can increase production speed through robots, but more important than that is securing a stable process flow. When people perform tasks, work speed and quality can vary depending on skill level and condition. By contrast, robots maintain a constant speed and motion under fixed conditions. This consistency greatly helps balance the entire production line. The speed of preceding and following processes must be predictable in order to efficiently connect inventory, logistics, inspection, and packaging.

Robots also play an important role in dangerous tasks. Work that involves repeatedly lifting heavy objects, exposure to high temperatures or dust, handling chemicals, or processing sharp parts places a burden on worker safety. When robots handle such work, the risk of industrial accidents can be reduced. Automation is not only a cost-saving measure but also an investment in safety. In particular, in workplaces where the workforce is aging, reducing repetitive physical strain becomes even more important. Robots can serve as tools that reduce the fatigue and risk accumulated in the human body.

The spread of collaborative robots is lowering the threshold for automation. Conventional industrial robots often moved quickly and powerfully behind safety fences. They required installation space, specialized programming, and process design. By contrast, collaborative robots are designed to work relatively safely next to people, making them easier to apply in small and medium-sized companies or smaller processes. They have high flexibility because they can assist workers, take over repetitive movements, and move to other tasks when needed. Behind the spread of automation from the exclusive domain of large corporations into broader industrial sites lies this kind of technological change.

For process stabilization, understanding the process into which robots will be introduced is more important than the robots themselves. If a process that is difficult to automate is forced into robotization, the expected results may not appear. Product design, parts supply methods, work sequence, inspection standards, and logistics flows must be organized in a way that fits automation. Automation is not about transferring a complex existing site directly into machines. Rather, it is a process of reducing unnecessary movements, simplifying work sequences, and identifying areas that can be standardized. Good automation begins with good process design.

Robot adoption can also become the starting point for data accumulation. Robots are not simply machines that move; they leave behind information such as work time, number of movements, error occurrences, downtime, and production volume. When this data accumulates, companies can identify bottlenecks in production lines, improve equipment efficiency, and predict maintenance timing. Judgments that once depended on the experience of field managers are transformed into data-based decisions. Robots are automation equipment and, at the same time, sensors that digitalize factories.

Manufacturing competitiveness has long been closely tied to low labor costs. Regions with low wages and abundant labor grew into the factories of the world. Companies moved production overseas to reduce production costs, and global supply chains were reorganized around cheap labor. However, this formula is gradually weakening. As demographic structures change, supply chain risks grow, and technological sophistication rises, it is becoming difficult to maintain competitiveness simply by producing large quantities in places where wages are low.

Automation changes the criteria for manufacturing competitiveness. What matters in the future is not how cheaply a company secures labor, but how stable and flexible a production system it possesses. Automated factories can secure a certain level of productivity even in regions where labor costs are high. They can maintain consistent quality, make delivery predictable, and respond quickly to changes in demand. These capabilities become even more important in an era when global supply chains are unstable.

Recently, companies have been reassessing their production bases. In the past, the common strategy was to move production sites in search of the lowest cost. However, the pandemic, geopolitical conflict, logistics disruptions, raw material supply instability, and trade regulations exposed the vulnerabilities of supply chains that were too long and too complex. Even if production costs are slightly lower, if parts do not arrive on time or logistics are blocked, the entire business can be shaken. As a result, companies are trying to move production closer to consumer markets, to places where supply chains are easier to control, and to regions with lower political risk. In this context, automation becomes a key condition that makes manufacturing possible even in high-wage regions.

The reorganization of manufacturing is also connected to national competitiveness. Countries with automation capabilities can maintain high-value-added manufacturing. In industries such as semiconductors, batteries, electric vehicles, biotechnology, precision machinery, and aerospace, precise process control and quality stability are more important than simple labor power. In these industries, automation and digital manufacturing capabilities are competitiveness itself. More important than how many factories a country has is how precisely those factories are operated.

Small and medium-sized companies also need to change. Large corporations already have the capacity to invest in robots, automation systems, and data systems. However, if the small and medium-sized companies that make up most of the supply chain fall behind in automation, the competitiveness of the entire industry also weakens. If the quality and delivery schedules of small and medium-sized suppliers become unstable, the production of large corporations is also affected. Therefore, industrial automation is not a matter for individual companies alone but a matter for the entire ecosystem. Support for technology, financing, workforce training, and standardization must be provided so that small and medium-sized companies can access automation.

Saying that automation reorganizes competitiveness does not simply mean that companies with more robots win. What matters is what kind of operational capability is secured through automation. Even when the same robots are introduced, some companies increase productivity, while others fail to fully utilize the equipment. The difference comes from process understanding, data use, participation by field workers, maintenance systems, and management strategy. Automation is not a competition over equipment; it is a competition over operational capability.

The reacceleration of automation is not happening only inside factories. Automation is also rapidly spreading across logistics and service sites. As online shopping has become part of everyday life, the workload of logistics centers has increased explosively. Customers want faster delivery, and companies must process more orders in a shorter period of time. Yet logistics sites are also experiencing labor shortages and high labor intensity. Sorting, moving, packaging, and loading goods are repetitive tasks that also place heavy strain on the body. In this environment, automation becomes a core means of improving logistics efficiency.

Logistics automation appears in many forms. Conveyor systems, automatic sorters, unmanned transport vehicles, autonomous mobile robots, automated warehouses, picking support systems, and packaging automation systems are entering logistics centers. Instead of continuously walking around vast warehouses to find products, people check and package goods brought by robots. When logistics flows are managed with data, inventory locations, order volume, shipping speed, and work bottlenecks can be identified in real time. Automation is the invisible foundation that makes fast delivery possible.

Automation is also becoming an everyday scene in the service industry. Kiosks in restaurants, unmanned ordering machines in cafes, self-check-in systems in hotels, automated reception systems in hospitals, unmanned payment systems in parking lots, and self-checkout counters in stores are no longer unfamiliar. This type of automation cannot be viewed merely as a device for reducing the number of workers. It reduces order waiting times during peak hours, decreases repetitive payment work, and allows employees to focus on customer service and quality control. It is also a method for maintaining service quality when labor is scarce.

The spread of serving robots and cleaning robots belongs to the same trend. The food service industry is one of the representative sectors suffering from severe labor shortages. It is difficult to find hall staff, working hours are long, and the intensity of work is high. Serving robots carry food close to tables, while employees handle final customer interaction and cleanup. This is not complete unmanned operation, but a way of sharing the workload. Cleaning robots are also becoming more useful in hotels, hospitals, airports, and commercial facilities that require repeated management of large spaces. When robots take on repetitive and physically demanding tasks, people can focus on more detailed management and customer response.

Service automation also changes the customer experience. At first, some people found kiosks or unmanned systems inconvenient, but over time, more customers have come to prefer quick ordering and simple payment. However, not all customers are familiar with automation. For older people or those unfamiliar with digital devices, automation can become a barrier. Therefore, service automation requires a balance between convenience and accessibility. Rather than designing everything to be handled without human help, it is more desirable to create systems in which human support is available when needed.

The spread of logistics and service automation broadens the meaning of industrial automation. Automation no longer refers only to robot arms inside factories. It is expanding into systems that reduce repetitive work and improve operational efficiency across various areas, including ordering, payment, movement, sorting, delivery, cleaning, guidance, inventory management, and customer service. Labor shortages and rising labor costs are accelerating this trend even further. Automation is spreading from a manufacturing technology into a way of operating society as a whole.

Whenever automation spreads, the biggest question raised is jobs. Do robots take away human work? Does automation make human labor unnecessary? This question cannot be answered easily. Some repetitive tasks will clearly decrease, and many jobs once performed by people will move to machines. In particular, simple, repetitive, hazardous, physically demanding, and standardized tasks will inevitably be heavily affected by automation. However, the reality of automation does not move only in the direction of people disappearing. Jobs disappear, but new jobs are also created, and the nature of existing work changes.

What matters in the age of automation is how the roles of people and machines are divided. Machines are strong in repetition, precision, speed, and consistency. People are strong in judging exceptional situations, forming relationships, solving creative problems, and making complex decisions. Instead of maintaining a system in which people directly perform every task, it is necessary to create a structure in which machines handle what they do well and people focus on what they can do better. This is the essential direction of automation.

Companies must redesign the flow of work as they introduce automation. If existing tasks are left as they are and only some parts are replaced by machines, confusion may actually increase. When robots enter the workplace, workers’ movement paths change, managers’ roles change, and new tasks related to maintenance and data analysis emerge. Production workers can move from being simple task performers to equipment operators and quality monitors, while field managers can shift from experience-based supervisors to data-based operators. Automation requires the reorganization of jobs.

Education and retraining are important in this process. For automation to succeed, field workers must be able to use technology without fear. They need the ability to operate robots, check errors, solve simple problems, and interpret data. Not everyone needs to become a professional engineer, but they must have the basic capacity to work with automation systems. If companies invest in automation equipment but not in training people, the equipment cannot be fully utilized. The success or failure of automation depends more on people’s ability to transition than on machines themselves.

The new division of labor between people and machines also makes us rethink the value of labor. Reducing repetitive and dangerous work can be a positive change. However, if people lose existing jobs in the process, social responses are also needed. If the productivity gains from automation are concentrated only in a few companies or in capital, inequality can grow. Conversely, if automation is designed in a way that improves worker safety, provides opportunities to move into better jobs, and strengthens the competitiveness of industry as a whole, a positive transition is possible.

Ultimately, automation is not only a matter of technology but a matter of choice. Robots can be used merely as tools to push people out, or they can be used as tools to complement human limitations. Even the same technology produces different results depending on the organizational culture and management strategy in which it is used. Companies in the age of automation must not focus only on reducing people. They must design automation in a way that makes human roles more valuable. When a structure is created in which machines handle repetition and people handle judgment and improvement, automation can become not a threat but a new foundation for industry.

The reacceleration of industrial automation is a difficult trend to avoid. Labor shortages will not be resolved easily, and rising labor costs will continue to pressure corporate cost structures. Gaps in skilled labor will increase quality risks, and supply chain instability will heighten the importance of production stability. Under these conditions, automation is no longer a distant future technology. It is a practical response to the problems industrial sites are facing now.

However, the direction of automation has not yet been determined. Whether automation becomes merely the language of workforce reduction or the language of production stability and labor transition depends on the choices of companies and society. What matters is not only how quickly robots are introduced. More important is what work is automated, what work remains with people, and what preparations are made so that people can move into better roles.

Future industrial competitiveness will not come from a confrontation between people and machines. It will come from how people and machines cooperate. This is an era in which people are becoming scarce, costs are rising, and production stability is becoming more important. Factories are calling on robots again, but the destination of those robots is not a future without people. It is closer to a future in which the structure of work changes. Automation is not a technology that erases people, but a technology that can share the industrial burdens that people are finding increasingly difficult to bear. The reacceleration of industrial automation stands at the starting point of that transition.

International Federation of Robotics, September 2025, Global Robot Demand in Factories Doubles Over 10 Years

McKinsey & Company, July 2025, The Robotics Revolution: Scaling Beyond the Pilot Phase

McKinsey Global Institute, November 2025, Agents, Robots, and Us: Skill Partnerships in the Age of AI

World Economic Forum, January 2025, The Future of Jobs Report 2025

Deloitte, November 2024, 2025 Manufacturing Industry Outlook

McKinsey & Company, April 2025, Empowering the US Workforce

MIT, October 2020, The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions

World Economic Forum, 2024, Reskilling Revolution: Preparing Workers for the Future of Automation

International Labour Organization, 2024, World Employment and Social Outlook: Trends 2024

OECD, 2023, OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market