DX Strategy
Change R2B
—Change the speed, scale and perspective—
In order to create new value from R2B, we will make full use of DX to change its speed, scale and perspective. To date, we have been promoting the use of AI tailored to specific research projects, and the number of cases leading to implementation has been increasing. Furthermore, in order to increase the speed and scale of research, we will establish a framework to oversee the use of AI across all of R2B and drive its implementation in a way that optimizes results for the entire company.
Transforming Research Activities and Accelerating Trial and Error
As a Trial & Error experiment driven company, our driving force is the trial-and-error approach of R2B: Try a large number of things and put in place the ones that work.
To ensure our competitiveness over the long term and drive the creation of new value, it is essential that we accumulate and effectively utilize all intellectual assets, including the failures that arise in the process. With R2B DX, we aim to triple the total volume of trial and error by 2029 (compared to FY2022).
In FY2025, the volume of research trial and error increased by 19% compared to FY2022. This achievement is primarily due to the widespread adoption of research record-keeping, which serves as the foundation for data analysis and AI applications. Additionally, the implementation of analysis and simulation tools for material design, as well as AI-powered systems for adjusting material properties and automating inspections in certain parts of the production process has also contributed.
Smart Work via Use of Generative AI
The adoption of AI is gradually progressing across various fields, led by the R2B Division. Approximately 2,000 employees, roughly half of our workforce, are currently using generative AI in their daily work.
We are also developing applications tailored to specific tasks, such as AI agents that handle administrative work and routine, repetitive tasks, AI-powered veteran operators, and risk assessments for manufacturing sites. Ultimately, we aim to create a work style in which people collaborate with AI assistants.
We are also focused on developing infrastructure, such as a new AI-ready data platform.
