Startup Interview

  • [Startup Interview] Professor Young Jae Jang, CEO of Daim Research, an Industrial AI and Digital Twin-based Smart Factory Solution Provider
  • 창업원
  • 2022-10-30 20:20:15
  • 218

We have a special guest for the startup interview! CEO Young Jae Jang is a professor of the Department of Industrial and Systems Engineering at KAIST, who not only works hard on research and fostering the next generation, but also pursues his dream of starting a business and runs an AI-based IT company. Daim Research, founded by Professor Jang and the research staff, is a Smart Factory solution provider company that develops AI algorithms applied to the manufacturing industry.

Can you briefly introduce Daim Research?

‘DAIM’ of Daim Research stands for ‘Data AI Manufacturing’. Our mission is to innovate manufacturing through data and AI. Daim Research is a research company established in February 2020 by me and three Ph.D.s from the Department of Industrial and Systems Engineering with the investment of KAIST. KAIST is the major shareholder of our company.

How did you decide to start a business while doing research and teaching students?

The people I respect the most are Thomas Edison and the Wright brothers. They are the startup entrepreneurs at that time. Most people think that the industry develops once academic theories are established, but in engineering it is often the opposite. After the Wright brothers succeeded in their first manned flight, the field of aeronautics was established (Related article:, and after Edison developed the incandescent light bulb, the first department of electrical engineering in the world was established at MIT (Related article:

Being a professor at KAIST for 10 years, I felt that the level of academics in Korea has increased. However, just like Edison created the electrical industries and the Wright brothers pioneered the aviation industry, it is time for research and development to transform the industry.

I think the role of KAIST is to pioneer new industries and create new paths, not just producing academic papers. That’s why most engineering professors at MIT and Stanford start at least one businesses.

As a professor at KAIST, many companies, regardless of medium-sized or large corporations, contact our research lab to innovate the manufacturing industry. Even though many academic theories were established 20 years ago, the field is still unaware of this. The manufacturing industry has the opportunity to innovate, and as a researcher of manufacturing, I decided to start a business to innovate the field.

Listening to Professor Jang’s story, Edison and the Wright brothers would have been scientists as well as entrepreneurs in this era! Just like they became pioneers, researchers will be able to innovate the field with the mindset of Professor Jang.

Daim Research applies a unique algorithm different from the existing algorithms in Smart Factory. What is the difference and characteristics of Daim Research’s algorithms?

I have experienced manufacturing automation for 20 years. The first 10 years in the industry, and the following 10 years in academics. Our smart factory algorithms are based on AI reinforcement learning and the digital twin technology.

Manufacturing AI and regular AI have different characteristics. For example, algorithms that determine anomalies in credit card payment fields and manufacturing fields are different. Irregular situations don’t occur frequently in manufacturing, so the anomaly data is significantly less than normal data. In other words, the problem of data imbalancing is serious. The key to solving these problems is domain technology. Unlike regular AI, manufacturing AI is only possible when domain information is combined in manufacturing.

In addition, while most AI companies focus on quality control and facility management, Daim Research focuses on production operations and manufacturing logistics. We utilize 100% of our own algorithms without using external libraries. To summarize, we have a unique technology based on manufacturing domain knowledge that also deals with problems in the industry.

I was unaware about the difference in manufacturing AI and general AI. Daim Research’s technology seems to have a unique direction from existing AI companies.

What is Daim Research’s core technology, and how do you expect the technologies to impact the industry?

The core of Daim Research’s technology is the manufacturing IT technology based on AI and advanced algorithms. We have AI+Digital Twin-based manufacture operating system developed by KAIST. The research staff already has 10 registered patents, and the AI-based smart factory solution was selected as KAIST’s top 10 technologies in 2019. Furthermore, it was selected as Google’s support project based on this technology.

We believe that our technology, which combines not only AI but also manufacturing domain knowledge based on our 20-year manufacturing know-how, is our competitive edge. The impact of these technologies on the manufacturing industry will be evident in the future. To begin with, IT technology in the domestic manufacturing sector is still far behind that of general IT technology. Most are still stuck in the IT paradigm of 20 years ago.

One of the major causes of this issue is the SI-outsourcing of manufacturing IT. There are more than 500 Manufacturing Execution System (MES), which is the core of manufacturing IT, S/W companies in Korea alone. Most of them only provide a concise UI/UX installation in DB. In other words, there are many opportunities to innovate the manufacturing field by applying AI and advanced algorithms, but most companies rely on SI outsourcing, so the overall industry has been slow to advance. This is the reality of the last 20 years. While domestic manufacturing IT companies focused on research and developing its own S/W, foreign S/W companies became in charge. Foreign solution companies, such as Siemens and Mitsubishi, has taken over in Korea. Here, the problem is that the foreign S/Ws are so expensive that it is difficult to utilize in companies other than large corporations. Even medium-sized businesses still operate on 20-year-old manufacturing IT systems.

Daim Research believes there is still a chance in the manufacturing IT field. Korea became a manufacturing powerhouse, with individuals’ labor. From now on, it is time to innovate using systems, not human labor. Our ultimate goal is to provide a foundation for manufacturing in Korea to take a leap forward.

We hope Daim Research’s technology innovates manufacturing IT and changes the paradigm. The day where domestic technologies stand tall in the industry will come!

How did you decide on this technology as the startup item?

Before I started the business, I already completed the verification of our AI Smart Factory Solution while collaborating with domestic conglomerates. We started this business to deliver the benefits of the solution to other companies, rather than developing solutions for each company. Our other AI-based smart factory solution is an AI logistics and return system, which has already been introduced in a domestic semiconductor automation industry and proved its business feasibility. Thus, we started the business after making sure that there is a market and that the product could be commercialized.

However, we did not start Daim Research solely based on these items and the domestic market. The manufacturing global manufacturing market is largely divided between the U.S. and China. The U.S. market is the center of product design technologies, and China is the center of hardware technologies. However, there aren’t a many manufacturing companies that have actual manufacturing sites in the U.S. Apple creates iPhones, but most parts and assemblies are done in Asia. There are manufacturing design and process technologies in the industry, but manufacturing operations are yet limited. And although China is growing rapidly, manufacturing S/W and IT technology are still behind the United States. Korea is one of the few countries in the world that have the technology and run manufacturing sites directly. It is an environment where global companies can emerge from the manufacturing AI field and smart factory sectors, and our AI-based manufacturing solutions can grow further.

You mentioned that robots are connected to each other drive automation in factories, and people and robots must coexist. If technology advances, people may lose jobs. How can robots and people coexist?

I don’t think people will lose jobs due to robots. First of all, the field of simple work has already been replaced by foreign workers. But we have to consider whether this work is something that humans are supposed to do. There are many times when people take risks in their current work. Rather, I think it is right for robots to replace people for human rights. Automation and connectivity will instead create new jobs. Daim Research is a good example ????

So, automation by robots is another opportunity for new jobs!

What are the advantages of a faculty startup?

Engineering technology must not only stay in papers. These technologies must be incorporated into the industry to innovate the field and increase the nation’s competitiveness. Faculty startups can effectively play a role in connecting new theories and industries.

This is a very important point. Many faculty members find it difficult to start a business, but it is necessary to introduce the technology to the world for the development of the industry. We recommend starting a business, while teaching students, as long as it is legally allowed.

What are the difficulties of faculty startup, and how did you overcome them?

Time is the most difficult part. Since I started this business, I have been working 80~100 hours. Whether it is research, corporate management, or manufacturing, one must know the field. Even before starting the business, I visited sites and discussed with field staff for at least one day. Now, I meet the field staff for at least 20 hours. I believe these tasks are helpful and necessary for research, education, as well as managing Daim Research.

The way to overcome the time issue is to hit three birds with one stone, by making one result lead to different goal outcomes. For example, instead of organizing data sets to be used in a manufacturing class, we use the processed data from actual Daim Research projects. If a vivid field experience can be accompanied while teaching students, operating Daim Research becomes preparing a solid class. Students are also highly satisfied because they can experience real data and examples.

I cannot imagine working 80 hours, while teaching students, doing research, and operating Daim Research. Professor Jang is truly the epitome of success. In addition, since Daim Research projects are used in class, students seem to indirectly experience the technologies used in field.

How should we supplement and promote faculty startups?

A change in perception of faculty startups is necessary. The thing that bothers me most is that many people see starting a business as a means of making money. In Confucianism, the idea that teachers must stay away from money makes people see faculty startups awry.

However, to truly innovate the industry, professors must not stay in the lab. One must feel the real changes and interact with the industry to make good research and education possible. Some may say that research should be done by universities, and application should be done by industry experts, but this is wrong. In the domestic industry, the number of experts who can read and apply theories in papers, especially in manufacturing, is very limited. The reason why MIT or Stanford is innovating is not because they write a lot of papers. Professors who are invited to world-class engineering, especially manufacturing conferences, are not invited because they wrote a lot of papers. Those who make real impact in the industry are recognized. In other words, I think that faculty startups at an engineering institute is a necessary factor for the development of academia and schools.

MIT and Stanford are known for their startups. There are a lot of startup supporting programs, and startups are open to everyone. We hope this startup culture is established in Korea and faculty startups become more active, leading to the development of the industry.

What is Daim Research’s ultimate goal and vision?

I hope Daim Research will contribute to Korea’s becoming of a new manufacturing powerhouse. Since Korea is the manufacturing powerhouse, I believe that there should be a no.1 company in the manufacturing IT and S/W field. This is our goal and vision.

Daim Research breaks the limits of existing manufacturing companies with its unique AI technology and builds smart factories. Professor Jang, CEO of Daim Research, started a business despite his limitations of his status as a professor, and suggests open innovation and the importance of collaboration between industries and universities. It is necessary to break the mold for research to lead to the industry. We hope Daim Research becomes the No. 1 company in the manufacturing and S/W field, and its success influences students to create an active startup environment at KAIST.

This was Professor Young Jae Jang of Daim Research.