As advances in AI garner attention, EBARA, which has a long history in manufacturing, also has data scientists who uses AI technology to analyze data. Employees who use AI and data to solve various business and operational issues across the company; employees who visit customer sites and solve problems based on data analysis involving semiconductor manufacturing equipment; employees who use AI technology to upgrade EBARA's waste incineration facilities—EBARA is home to human resources who utilize these technologies in various fields, and we are also working to nurture young talent.
What these employees are conscious of on a daily basis is not the definite use of AI but rather how to clarify the issues at work and then thinking about how AI can contribute to their solutions. Sharing their opinions are three individuals involved with AI at EBARA: Keisuke Kawashima (Data Science Section, Corporate Data Strategy Team), Junya Machida (New Technology Development Section, Development Department, Core Technologies Group, EBARA Environmental Plant Co., Ltd.), and Yoshimasa Osone (Data Science Section, Equipment Division, Precision Machinery Company).
<br><br><br>Kawashima—I am a member of a team that works on solving issues across the company based on AI and data strategies. In addition to promoting transformation using data in the manufacturing and after-sales service fields of various departments, we work together with departments to solve problems that exist between departments as well as issues specific to departments. I am involved with many activities with the Components Division of the Precision Machinery Company right now.
Keisuke Kawashima
Ozone: I analyze data collected from EBARA semiconductor manufacturing equipment used by customers to improve customer productivity and work efficiency. For example, I work on abnormality detection by understanding operation patterns and trends in the equipment with data. The aim is to improve our customers' manufacturing sites by combining semiconductors, AI, and software.
Machida—My work involves improving the quality of EBARA products and services using AI and ICT technologies such as machine learning. Currently, I am mainly working to upgrade and save labor in the operation and management of waste incineration facilities that EBARA is entrusted with by customers. At EBARA Environmental Plant, we are promoting joint development with a AI start-up, and we are developing our own technologies for the areas we can handle. For example, we developed a system where AI automatically identifies the type of waste and automates crane operations, which previously relied on an operator's experience and judgment. In addition, we developed a system where AI automatically detects signs and occurrences of falls based on camera images and video in response to the risk of people falling into pits where waste is stored at waste incineration facilities.
Kawashima—I chose to work at EBARA as a mid-career hire mainly because of the team's broad promotion of data utilization. I started working with AI and data when I worked for a manufacturer in my previous job. With the launch of a smart factory project, there were efforts to utilize the data collected from the plant, and I joined as a member. I was a beginner in data science, but the psychological hurdle to handling data was small because I had simulated chemical systems in college.
From there, I created a system for quality inspections and abnormality detection using data. This was more challenging than I expected. Gradually, I wanted to work more on promoting decision-making that uses data, and when I learned that EBARA had such a specialized team, I changed jobs.
Ozone—I applied when I saw that EBARA was recruiting new graduates for data scientist positions. I originally researched martial arts in graduate school. I was working on using AI image processing technology to predict the next movement based on the physical condition of an opponent. When I mentioned this in the job interview, my current boss at EBARA told me the company could use that technology, and so I decided to join EBARA. It took a little courage to move from sports to the manufacturing industry, but that assurance helped me make up my mind.
Machida—In your department, Ozone, were many of your colleagues engaged in data science when they were in college?
Ozone—No, not many. Some studied data science as a hobby or through activities in their private lives while working at other jobs. At the same time, since my generation, because there are data scientist positions for new graduates, there are some people who have had experience since they were students. People from various careers are working together.
Machida—I joined the company in 2017, but there were no data scientist positions at that time. I wanted to design equipment in the field of plant engineering myself. However, the year I joined the company, my current AI-related department was launched, and I was assigned there. It was unexpected, but like Kawashima, I began with AI image recognition and data analysis, which was a lot of fun right away. Since information about AI was not as widely available as it is now, I learned on my own and enhanced my knowledge and skills by cooperating with AI companies, partners with whom we were co-developing.
<br><br><br>Machida—In my case, the system we developed will be used by EBARA employees who are engaged in the operation management of waste incineration facilities. But rather than being developed unilaterally by us, we are conscious of implementing the system based on the understanding that the site will be satisfied with it, that is, the system will be truly easy to use, and that it will solve the problems on site.
Junya Machida
In order to keep current infrastructure operating in the face of a shrinking workforce, automation and efficiency through AI are inevitable. That said, each person working on site has experience and thoughts that they have developed in their respective jobs. At first glance, automating work may feel like less work for people on site.
It is important that we make systems that contribute to reducing workloads and improving productivity on site, and that people can use it with peace of mind.
Ozone: Similar to what we are talking about now, when we visit a customer's site, even if the data is already available and we can see that we can create a given function, we always take a step back and think again about whether the customer really needs that function. Supposing we create a new function, ideally we are specific about what kind of results it would produce and how much it would contribute to the business, and then we would discuss this with the customer.
For this purpose, we do not only talk about hypotheticals; we also create systems on a small scale and at low cost, and discuss with our customers whether it is necessary based on the results.
Kawashima: I think what we are talking about is very important. Because there are times when people approach us and ask things like “Can you do something with the data we have?” or “Can we do something with AI?” but this is not the correct way to move forward. The first thing we want to clarify is the purpose of what problems we have and how we want to solve them. With that purpose in mind, we can then think of AI and data as the means to solve the problems.
Expectations for AI are increasing these days, but this technology does not make anything and everything possible. Even if there is a large amount of data, without a determined problem, it becomes difficult to know where to start. If we do not start with a problem, we may have a large amount of data but no numerical value to create a solution.
Ozone: In that sense, instead of starting with AI or data, I think it is ideal to first start by listing the problems that people working on site are facing, and then think about what can be done with AI.
Kawashima: Right. I think it would be nice to have our team as a place to consult, so I would like to have people start by reaching out to us and saying, I have this problem. Even an email would be fine. Then, I would like to solve that problem together with the people working on site. We may be familiar with data, but the people working on site are more knowledgeable about their business and the issues they face.
Ozone: By working together, the people working on site will gain knowledge of data science. It benefits both parties.
<br><br><br>Ozone—In my field, someone who is familiar with equipment will have a considerable advantage if they also become familiar with AI and data. As Kawashima mentioned earlier, expectations for AI are sometimes too high. So, if someone who understands both on-site realities and AI, they will be able to make more realistic proposals to customers. A realistic system is more likely to be effective, so if such a system becomes widespread, it will become a strength for EBARA.
Yoshimasa Ozone
Machida—I think what makes EBARA interesting is that we can think about AI in terms of how to use it in response to problems facing customers and social issues. For example, a company that specializes in AI development may mainly create AI models and systems that are requested by customers. But we can start by not only creating models but also thinking about how to use AI to solve problems based on more fundamental concepts.
In particular, I am working on upgrading waste incineration facilities, so I am considering implementing AI to contribute to solving social issues such as decarbonization and environmental issues. There is a real pleasure in tackling big things.
Kawashima: The great thing about EBARA is that, as you can see from these two talk, we can be involved in various areas through AI and data. In addition to semiconductors and the environment, there are of course pumps and many new businesses. Data scientists are intellectually curious and like to analyze using a wide variety of data, so I think the breadth of the company's business fields is fascinating.
Kawashima—I would like to be able to make EBARA pumps, blowers, and other products that we have shipped traceable. I would also like to be able to obtain data after shipment. Efforts toward these goals have already begun. I believe we can build better products and sales systems based on such operating data.
Ozone: I want to become better at communicating to customers about how AI and data science work and their benefits in an easy-to-understand manner. In particular, AI is an unknown for customers, and there are times when we are asked to explain how it works in detail. In these cases, an explanation that is too technical will not help them understand. But on the other hand, too much simplification will not lead to acceptance or trust. I want to develop the ability to convey information that is easy to understand and that is convincing.
Machida—With regard to garbage and waste, decarbonization and tackling environmental issues will become more and more important going forward. We cannot continue our current methods 20 or 30 years in the future. I want to create a new type of waste incineration facility by incorporating not only AI but also new technologies and ingenuity. Currently, I am working on EBARA equipment, but I would love it if we could make great products and see them adopted outside of the company. I would like to contribute to solving social issues through such products.
※Department names are as of 2023/12
Keisuke Kawashima
Data Strategy Team
Junya Machida
EBARA Environmental Plant Co., Ltd.
Yoshimasa Ozone
Precision Machinery Company
Read the cross-talk here