The adoption of AI (artificial intelligence) is rapidly progressing in Japan's core manufacturing industry. Tasks that previously relied on the experience and intuition of skilled technicians are becoming automated and more highly accurate thanks to advancements in AI. This article explains AI case studies, costs, risks, and implementation steps for DX promotion managers and business planning managers in the manufacturing industry who are considering introducing AI.
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Reasons and challenges for the slow adoption of AI in the manufacturing industry
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How to proceed when introducing AI in the manufacturing industry
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Why is it difficult to utilize AI and data in the manufacturing industry?
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Connecting data has great value in the manufacturing industry.
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AI will not change business processes, but rather "organizational knowledge."
What is AI in manufacturing?
In manufacturing, AI is an initiative that aims to formalize the tacit knowledge inherent in every stage of production—such as optimizing inventory ordering, optimizing production lines, quality control, and demand forecasting—through AI-driven processes, thereby improving operational efficiency and creating new value. Its essence lies not simply in replacing tasks with machines, but in inheriting and utilizing the know-how accumulated on the factory floor as data.
The reasons why AI is attracting attention in the manufacturing industry
The primary reasons why the introduction of AI is urgently needed in the manufacturing industry are the severe labor shortage and the aging and declining number of skilled workers. As the challenge lies in how to pass on the tacit knowledge and advanced techniques that have been cultivated over many years to the next generation, the movement to digitize and systematize these techniques using AI is accelerating.
Furthermore, with the intensification of global competition, traditional methods are no longer sufficient to cope with the need to balance quality and speed, as well as flexibility in customization and on-time delivery, which is a major factor driving the adoption of AI. According to the 2026 White Paper on Manufacturing, while approximately 70% of businesses acquire data on their manufacturing processes, only about 40% have seen results from utilizing the acquired data. The main obstacles cited for AI and digital adoption are "acquisition of knowledge and know-how" (57.2%) and "difficulty in securing human resources" (47.9%).
What AI can do in manufacturing
AI excels in a wide range of areas within the manufacturing industry. Typical examples include "quality inspection (visual inspection)" which analyzes camera images to identify defective products, "demand forecasting" which derives future needs from past sales data and market trends, and "predictive maintenance" which detects signs of failure from machine operation data.
Recently, the application of AI to office work and development tasks has expanded to include automating manual creation using generative AI and supporting idea generation in design and development.
Key applications of AI in the manufacturing industry
Here, we introduce typical AI application examples in the manufacturing industry, categorized by process. Try to get a better understanding of how AI can help solve your own company's challenges.
Automation of visual inspection using image recognition
This is an example of introducing image recognition AI to inspect products for scratches and dirt, a task previously performed manually. While many companies have been working on this topic for some time, the high level of human skill required meant that many challenges had to be overcome to actually implement and achieve effective results, including material handling issues, imaging environment issues, and external disturbance issues. As a result, there were many cases where implementation proved difficult. However, with advancements in peripheral technologies such as the processing power of edge devices, the number of cases where AI can be practically implemented and used is steadily increasing.
AI-powered demand forecasting and inventory optimization
Many companies have tackled this theme in the past, but there are many factors that need to be considered. Even with the same information, there are many business decision-making factors, such as the timing of when to strategically avoid lost opportunities or when to reduce assets. The data that needs to be looked at and the points of judgment that need to be considered vary widely depending on the product characteristics, market, and customer, making it extremely difficult to build an AI model or system that can withstand practical use.
Currently, with the rapid evolution of generative AI agents, there are cases where they can be used to assist decision-making by assigning agents to specific product characteristics, etc., to gather information, format data, and provide insights, while keeping on-site experts as supervisors, with the goal of reducing the workload of those experts and allowing them to focus on tasks that require more judgment. This approach can reduce workload and facilitate the expression of tacit knowledge.
Predictive maintenance using sensor data
Sensors attached to manufacturing equipment collect data such as temperature and vibration in real time, and AI detects signs of abnormalities. By preventing sudden equipment shutdowns (downtime), it becomes possible to ensure stable operation of the production line and optimize maintenance costs.
This theme is also becoming more practical thanks to advancements in peripheral technologies, such as the development of applications for handling abnormal situations, including workflows and notifications for contacting users to seek decisions, and calibration at edge terminals that take into account device differences and resonance.
Automation using industrial robots (AMRs)
While the integration of AI into AMRs and industrial robots is still in its early stages compared to the use of AI that does not involve physical movement, advancements in peripheral technologies such as improved computing power and evolution of robot learning environments are reducing dependence on external systems integrators (SIers) and enabling on-site learning and operation. Although various companies are still exploring promising use cases for humanoid robots other than picking, the field is evolving rapidly, and many companies have begun trials.
This not only addresses labor shortages, but also improves occupational safety and health by replacing dangerous tasks with robots.
Benefits of introducing AI in manufacturing
The introduction of AI brings various benefits to manufacturing sites. Here, we will explain three major advantages.
1. Increased productivity and reduced costs
The biggest advantage is increased productivity and cost reduction through improved operational efficiency. This includes not only reduced labor costs through automation, but also reduced material waste through improved yield and lower equipment repair costs through predictive maintenance, ultimately contributing to the optimization of total costs across the entire factory.
2. Quality improvement and elimination of reliance on individual expertise
AI is fatigue-free and consistently performs tasks and makes decisions according to a fixed standard. Therefore, it can reduce human error and ensure consistent product quality.
Furthermore, by standardizing the decision-making criteria (intuition and know-how) that previously depended on specific skilled workers into AI models, we can eliminate the reliance on individual expertise in our work and address challenges in technology transfer.
3. Streamlining on-site operations
For companies aiming to improve the efficiency of their indirect departments and on-site staff, the use of generation AI and similar technologies is particularly effective.
By utilizing AI generation and other technologies, on-site ancillary tasks can be significantly streamlined, such as automatically creating daily reports and other reports, suggesting solutions from past troubleshooting history, and automating internal inquiry handling (AI chatbot).
Reasons and challenges for the slow adoption of AI in the manufacturing industry
While AI offers many benefits, there are also many cases where AI implementation does not proceed as planned or even fails. Here, we will explain some common challenges.
1. Initial costs and return on investment
Traditional AI (developing dedicated models, equipment, and data preparation) requires a considerable initial investment. On the other hand, generative AI can be piloted using an API for a monthly fee of a few thousand to tens of thousands of yen, thus lowering the initial hurdle for verification.
However, in the production phase, such as company-wide deployment, additional system investments may be required to cover cloud infrastructure, database maintenance, and other related costs.
Another common challenge for both parties remains the difficulty in calculating ROI (how much profit will be generated). It is important to consider the return on investment after clarifying the tasks that you want to solve with AI and the costs incurred so far.
2. Lack of data and difficulty in data maintenance
The performance of conventional AI depends on the quality and quantity of training data, so challenges include insufficient defective product data, paper-based management, and inconsistencies in data formats.
On the other hand, while generative AI does not require data collection from scratch because it utilizes a pre-trained LLM model, RAG construction and fine-tuning are required separately to leverage company-specific information.
As a prerequisite, the key to successful implementation lies in "structuring and cleansing internal data," such as digitizing paper forms and standardizing document formats that differ from department to department.
3. Shortage of personnel and lack of understanding on the ground.
There is a serious shortage of IT professionals and data scientists who can understand AI and drive projects forward. Furthermore, even when systems are built, gaps often arise between the system and the field, such as "field workers not trusting AI" or "being unwilling to change established methods," resulting in cases where the system fails to take hold.
4. Risk of remaining at the PoC stage
Many companies proceed to the PoC (Proof of Concept) stage with the intention of "let's try out AI," but end up in a state known as "PoC death" (stopping at PoC) because they don't achieve the expected accuracy or the implementation costs in the production environment become enormous, preventing them from actually putting the AI into operation. This risk increases when AI is introduced without a clear purpose.
5. Risks specific to generative AI
Generating AI can sometimes exhibit "hallucination," confidently outputting content that is contrary to the facts. Particular care must be taken to prevent the inclusion of misinformation in specifications and safety standards. Furthermore, while the use of a secure environment for businesses where input data is not used for external training is a prerequisite, when linking internal data (RAG construction), it is essential to design an architecture that strictly links with internal access permissions and prevents employees from "unauthorized extraction of confidential information."
How to proceed when introducing AI in the manufacturing industry
To overcome the challenges mentioned above and successfully implement AI, it is crucial to take the right steps.
The importance of starting small
Instead of aiming for a large-scale system overhaul of the entire factory from the start, the key to success is a small-scale start, focusing on specific lines or processes.
In particular, since the generation AI can utilize a pre-built SaaS-type generation AI service for businesses, it is possible to conduct trial implementations and verify their effectiveness at low cost and in a short period of time by first piloting non-productive tasks such as "internal chatbots" and "automatic daily report generation."
By accumulating small successes (quick wins), it becomes easier to verify the return on investment and to gain understanding and cooperation from those on the ground.
Selecting the Right Partner
If your company lacks sufficient AI talent, selecting an external partner with domain knowledge (business knowledge) in the manufacturing industry is essential.
It's crucial to choose a partner or startup that not just develops systems, but deeply understands the challenges of the manufacturing floor and can work alongside you to support data utilization and AI implementation.
At Macnica, we leverage our extensive expertise in the fields of AI and IoT to support our customers in solving their business challenges.
Internal systems and data preparation
To advance the project, it is necessary to establish a cross-functional organizational structure (such as a DX promotion team) that brings together personnel from the information systems department, management planning department, and manufacturing site. In parallel, let's define rules for data collection and storage with a view to future AI utilization, and proceed with developing a data infrastructure that can continuously acquire high-quality data.
Why is it difficult to utilize AI and data in the manufacturing industry?
With the advent of generative AI, many companies are beginning to utilize AI. However, in the manufacturing industry, there are many challenges such as "AI has been introduced but has not led to the expected results" and "the proof of concept (PoC) was successful but cannot be rolled out company-wide." This is due to the complex business structure unique to the manufacturing industry.
The manufacturing industry has an extremely long value chain from market analysis and marketing to research and development, product design, procurement, production planning, manufacturing, quality assurance, logistics, sales, and maintenance and service, all the way to getting a single product to the customer. Each area has organizations with a high level of expertise, and operations are optimized under different KPIs and evaluation metrics.
Furthermore, each department has a history of implementing its own optimal system, such as PLM, CAD, ERP, MES, SCM, and CRM. As a result, even when handling the same products or parts, management methods and data formats differ from department to department, leading to "data silos" where information is fragmented. Many companies experience rework due to design changes not being immediately reflected in procurement, manufacturing, quality, and service, and rely on the experience of skilled personnel for coordination between departments.
AI thrives on high-quality, consistent data. However, in environments where data is fragmented across departments, AI cannot understand the entire enterprise. While it may contribute to streamlining individual tasks such as design support and quality analysis, it struggles to optimize the company as a whole.
In other words, the fundamental challenge in using AI in manufacturing is not the AI technology itself, but the lack of connectivity between departments. AI utilization in manufacturing is not simply about automating tasks, but about creating a system that allows for consistent use of information across the entire company.
Connecting data has great value in the manufacturing industry.
While the manufacturing industry is one where AI is difficult to implement, its complexity means that the value gained from connecting data across departments is significantly greater compared to other industries.
For example, if design changes occur and that information is shared in real time across procurement, production planning, manufacturing, quality assurance, logistics, and service, it becomes possible to streamline the process from reviewing parts procurement and optimizing production plans to updating quality standards and work instructions and revising maintenance manuals. Tasks that were previously handled individually by each department can now be linked through data-driven collaboration, leading to company-wide results such as shorter lead times, improved quality, reduced inventory, and increased customer satisfaction.
Furthermore, the value of AI increases dramatically as data becomes more interconnected. By learning across a wide range of data, including not only design information but also production results, quality data, maintenance history, and customer feedback, AI can provide insights that would not have been possible for individual departments to obtain, such as "predictive signs of quality defects," "designs prone to failure," and "optimal maintenance timing."
The key is not to individually implement AI in each department. Optimization of the entire company is only achieved when design AI, production planning AI, quality AI, sales AI, and service AI all refer to common data and collaborate to make decisions. This aligns with the concepts of digital threads and AI agents, which have gained attention in recent years.
In the age of AI, competitiveness will not be determined by "how many AI systems are implemented," but by "how well data can be connected across departments, and how effectively humans and AI can share the same information." Because manufacturing is so complex, the competitive advantage gained from achieving this will be even greater than in other industries.
How the era of generative AI has changed the way we approach digital transformation in manufacturing.
In the manufacturing industry, the importance of connecting data from design to service has long been discussed. However, achieving this often requires large-scale system overhauls and long-term infrastructure development, making it difficult for companies with existing systems and departmental operations to take the first step.
Furthermore, investing in data infrastructure presents the challenge of not being able to easily see the results. For a business to make continuous investments, it is essential to feel the effects at an intermediate stage and build on small successes to fuel the next investment. However, traditionally, even though the idea of "starting small and growing big" was understood to be ideal, preparing for a Proof of Concept (PoC), system development, and data preparation required a lot of time and effort, and it was not uncommon for it to take several months to several years to achieve the first successful experience. As a result, there were many cases where projects stalled before the effects could be felt.
The emergence of generative and agentic AI has dramatically changed this situation. The widespread adoption of AI that can understand business processes using natural language and leverage existing systems and data across them has dramatically reduced the time and effort required to quickly prototype ideas and verify their effectiveness in the field. This has made small successes, which were previously difficult to achieve, a reality, and has made it easier to adopt an approach of gradually deploying them company-wide while confirming results.
Even more importantly, the role of AI itself is changing. Traditionally, AI focused on streamlining routine tasks and simple work within each department, but now it is becoming possible to oversee entire business processes across departments and redesign operations through collaboration between humans and multiple AIs. AI is evolving from a mere replacement for tasks to a force that transforms business processes across entire companies.
On the other hand, the manufacturing industry demands extremely high reliability in areas such as quality, safety, traceability, legal regulations, and security. Therefore, it is not realistic to entrust everything to AI. What is important is to understand your own business model, operational processes, IT architecture, and security requirements, define the scope within which AI can make appropriate decisions, and design "guardrails" where humans will make the final decisions.
What the manufacturing industry needs going forward is not just people who can use AI, but people who can understand operations, data, IT, and AI across the board and envision overall optimization. In the age of generative AI, competitiveness will depend not only on the performance of the AI, but also on whether we can cultivate systems that allow for the safe use of AI and the people who can design and operate them.
AI will not change business processes, but rather "organizational knowledge."
The benefits of introducing generative AI and agentic AI are not limited to efficiency improvements such as "reduced working hours" and "automation of tasks." In the manufacturing industry, the greatest value lies in making tacit knowledge, which previously relied on individual experience and intuition, visible and transforming it into knowledge that can be utilized throughout the entire organization.
In manufacturing, daily operations are not supported solely by information registered in systems. Judgments such as "this equipment tends to stop under these conditions," "this customer prefers these specifications," "this design change is likely to lead to quality problems," and "consulting with this department beforehand will ensure smoother subsequent processes" are often accumulated through human experience. This kind of knowledge is not written in manuals, but is passed down through cross-departmental coordination, years of on-the-job experience, and person-to-person communication.
Especially in the manufacturing industry, veteran employees who have worked across multiple departments often possess a wealth of important decision-making criteria that are not reflected in the system. Those who manage operations across multiple systems such as PLM, ERP, MES, and CRM understand the connections between tasks that are not expressed in data, and this knowledge is what underpins a company's competitiveness.
Agentic AI has the potential to gradually learn decision-making processes and work methods through interactions with people and daily work processes, and to organize this knowledge as explicit knowledge. Rather than AI replacing tasks, it is expected to visualize the thought process itself—"why that decision was made" and "what information was used to make that decision"—and transform it into an asset that can be reused throughout the organization.
Its value becomes even greater when considering the current social environment. With a declining workforce and a mass retirement of skilled technicians, opportunities for knowledge sharing that previously occurred naturally through on-the-job training (OJT), casual conversations in smoking rooms, breaks, and company social gatherings are decreasing. In a world where human interaction is becoming less frequent, passing on tacit knowledge to the next generation has become a major management challenge for the manufacturing industry.
Therefore, the role of AI is not merely to improve operational efficiency. It is to extract the experience and judgment that people have cultivated over many years, share it across departments, and accumulate it as knowledge for the entire organization. AI is not a replacement for humans, but rather a partner in transforming human knowledge into organizational strength.
In the future, the competitiveness of manufacturing will not depend on how much AI is implemented, but on how effectively tacit knowledge can be transformed into organizational knowledge through AI. Connecting data, connecting operations, and connecting human experience to the future—that is surely the greatest value of manufacturing DX in the age of AI.
The future and prospects of AI in manufacturing
The use of AI in manufacturing is still in its early stages of development. In the future, it will likely evolve into more advanced and broader areas, such as sophisticated real-time processing, innovation in design and development processes using generative AI, and optimization of the entire supply chain through data sharing between companies.
At Macnica, we continuously propose the use of AI to solve our own challenges while closely monitoring the latest technology trends. If you are considering introducing AI into your manufacturing business, please feel free to contact us.