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[Examples of digital technology utilization in the chemical industry] Improving profitability and productivity through MI and RPA

About the chemical industry

The chemical industry is one of the essential industries for the development of modern Japan.

The chemical industry has become a major industry with a shipment value of 40 trillion yen, accounting for about 14% of the total manufacturing industry. Chemical products are materials that make up products that are essential to our lives, such as automobiles, liquid crystal displays, and food films. I can say

In addition, the chemical industry is a globally competitive industry. Manufacturers in Europe and the United States are conducting M&A specializing in business areas in an effort to strengthen their businesses and increase profits. Furthermore, basic research is also underway in so-called emerging countries such as China and South Korea. If you develop a product first, you can take the global market share at once, so the competition is extremely fierce.

In addition, from the perspective of accelerating research and development, the use of materials informatics (MI) is spreading in the chemical industry. Materials informatics uses machine learning to speed up the development of new materials.

In this way, the chemical industry is required to develop products at a speed that cannot be compared with conventional speed while utilizing new technologies.

Challenges for the chemical industry

There are two major issues in the chemical industry, where rapid business development is required.

(1) Securing earnings and improving profitability

②Improve productivity by utilizing MI, etc.

Issue 1 for the chemical industry: Securing earnings and improving profitability

According to the Ministry of Economy, Trade and Industry, Japanese chemical manufacturers have lower profitability than foreign companies. The German chemical manufacturer BASF has a profit margin of 14%, while the Japanese manufacturer’s profit margin is about 5%.

If the rate of return is low, the money that can be spent on capital investment and research and development costs will decrease, making it impossible to maintain competitiveness.

According to a survey conducted by the Ministry of Economy, Trade and Industry, “Situation Surrounding Technological Development in Japan’s Industrial Sector,” the growth rate of R&D expenditures by Japanese companies is at a lower level than overseas. Low R&D spending growth may have contributed to the slump in sales.

In order to secure sufficient capital investment and research and development expenses, it is necessary to secure profits and improve profitability.

Issue 2 in the chemical industry: Increasing productivity in chemical product development

Improving the productivity of chemical product development is also a major issue.

Materials informatics, mentioned above, is one of the methods that is considered day-blind. Utilizing MI is expected to speed up the development of new materials. Its effects have already been highly evaluated, and it is expected that materials informatics will be generalized in the future.

In Japan, the Ministry of Economy, Trade and Industry (METI) has set a future vision of “a country that can contribute to the world by creating new value and industries with materials”, and the development of chemical products is an important theme for the country.

Furthermore, the promotion of “RPA (Robotic Process Automation)” is also attracting attention to improve the efficiency of work related to research and development. The use of RPA is not only effective in improving productivity, but is also effective in resolving labor shortages in the near future.

There is a need to further improve productivity by skillfully incorporating digital technologies such as MI and RPA into product development and business processes.

Examples of the use of digital technology in the chemical industry

The use of digital technology is gradually spreading in the chemical industry as well.

This section introduces three examples of the use of digital technology in the chemical industry.

Efficiency of R&D by MI (Materials Informatics)

Until now, the development of new materials in the chemical industry has largely relied on the experience of researchers, but in recent years there has been a shift to a method of proceeding with development based on data.

MI (Materials Informatics) is a method for advancing development by effectively utilizing that data. Utilization of MI will lead to more efficient and faster development of new materials.


Sumitomo Chemical Co., Ltd. material development example


Product lifecycles were getting shorter, and customer requirements were becoming more fragmented and sophisticated. As a result, we were not able to meet the needs with the “experimental supremacy” development method.


We combine all physical properties and structural data of materials by utilizing MI from the principle of experiment supremacy. Analyze and parse the combined data to derive the optimal solution.


When developing the heat-resistant polymer (copolymer), there were one million combinations of the amount ratio, but as a result of introducing MI using “Bayesian optimization”, the number of experiments was 20 times and the optimal combination succeeded in discovering

The combination discovered was a combination that the researchers had not thought of, so it proved the practicality of MI.

Practicing RPA to improve operational efficiency

Many people spend their work time on a huge amount of paperwork. However, most of the paperwork is repetitive work and can be automated with RPA.

RPA is a software that automates data entry, transcription, and mail delivery at a fixed time, which was conventionally done manually. RPA has been introduced in many industries, and even in the chemical industry, it has been introduced to improve the efficiency of back office operations.


Case study of introducing RPA in 17 operations of accounting/finance, human resources/general affairs of Teijin Limited


There was no manual for window work to apply for modification of supplier code, and there was a lot of variation in work quality, which was a burden.

In addition, to improve productivity, we wanted to automate simple tasks such as sending e-mails related to working hours management and reconciliation processing of overdue receivables.


By introducing RPA and automating work with robots, we aimed to improve work quality and reduce work burden.

Furthermore, we identified the operations within the company and introduced RPA in 17 operations with the aim of improving productivity.


After introducing RPA, in addition to improving the work quality of modifying supplier codes, we achieved an 80% reduction in man-hours from 60 hours per month to 12 hours. In addition, it seems that it has also led to the release of the psychological stress of the worker, “I must not make a mistake.”

It is estimated that more than 3,000 hours of work hours will be saved per year by converting 17 tasks to RPA, and the company has a good reputation.

In the future, we would like to introduce RPA to 300 tasks and have robots handle tasks equivalent to about 100,000 hours.

laboratory automation

R&D experiments take a lot of man-hours. It involves putting a substance into a container, changing the volume, conducting an experiment, and collecting data.

R&D does not immediately produce the desired results. Good results can only be obtained through the steady accumulation of research and development efforts. However, the work itself is just a matter of changing the conditions little by little, and we want to reduce the burden and time on researchers.


 Experimental example of “A mobile robotic chemist,” an automated experimental robot developed by a research team at the University of Liverpool, UK


By conducting experiments faster and more accurately than humans, we have improved work efficiency. Also, by introducing AI, we wanted to further improve efficiency by making it possible to determine how to change the capacity and environment based on the experimental results without human intervention.


We have deployed robots that move around the lab and perform experimental tasks automatically.


 “A mobile robotic chemist” can continue to operate for 21.5 hours a day, and in an experiment to efficiently generate hydrogen, it operated for 172 hours in 8 days and succeeded in conducting 688 experiments.

It is said that it would take several months for a person to perform a similar number of experiments, demonstrating the efficiency of work. In addition, the experimental data is continuously updated, and it is also possible to determine the next optimal experiment based on the experimental results.


Since the R&D cycle is accelerating in the chemical industry, it can be said that the introduction of MI and RPA is essential. Rather than relying on the experience of conventional researchers, there is also a demand for data utilization to promote research and development with higher potential based on past data.

The chemical industry still plays a major role today, but further rapid development is expected if researchers and engineers can devote more time to what they should be doing. Materials developed in the chemical industry are used in other industries, leading to the development of other industries.

For the further development of society, we must keep an eye on trends in the use and application of digital technology in the chemical industry.

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