TRUSCO Nakayama and Fujitsu Accelerate HR Transfer Decision-Making with Data and AI
TRUSCO Nakayama and Fujitsu have developed an application using Fujitsu's 'Fujitsu Data Intelligence PaaS,' AI, and mathematical optimization models to reduce HR transfer planning workload by approximately 98%. Implementation began with the April 2026 personnel transfers.
📋 Article Processing Timeline
- 📰 Published: May 20, 2026 at 19:20
- 🔍 Collected: May 20, 2026 at 11:01
- 🤖 AI Analyzed: May 20, 2026 at 11:13 (11 min after Collected)
TRUSCO Nakayama Corporation and Fujitsu Limited have accelerated decision-making in TRUSCO Nakayama's personnel transfers by utilizing Fujitsu's 'Fujitsu Data Intelligence PaaS,' an all-in-one operation platform that integrates data utilization through to operational execution under Fujitsu's 'Uvance' business model. In this initiative, the companies built an application in approximately four months that proposes personnel transfer plans reflecting TRUSCO Nakayama's diverse HR systems and the judgment criteria of HR personnel, using AI and mathematical optimization models. Implementation began with the personnel transfers in April 2026.
Background
TRUSCO Nakayama has been pursuing a talent strategy that promotes individual employee growth and organizational optimization through cross-departmental transfers. In considering these plans, the company has made placement decisions considering both individual growth and total organizational optimization. Furthermore, as part of creating a work environment where employees can feel secure and work for a long time, the company has established diverse personnel systems, which has led to increased complexity in the matters to be considered during transfers. As a result, formulating transfer plans based on these elements was highly time-consuming.
Overview of the Initiative
Given the nature of HR work, which relies heavily on experience, tacit knowledge, and judgment in specific situations, Fujitsu's Forward Deployed Engineers (FDE) embedded themselves on-site. Working alongside TRUSCO Nakayama's HR department, they organized the HR transfer workflow and business processes. Following this, both companies worked on accelerating the HR transfer decision-making process using data and AI, rapidly developing an application that proposes transfer plans accounting for complex factors.
This application features the following three key characteristics, contributing to the efficiency of decision-making in TRUSCO Nakayama's personnel transfers by supporting HR staff in planning and evaluating placement proposals:
1. Centralized Management of Scattered Information
The application centralizes personnel data previously scattered across multiple internal systems and Excel files onto the 'Fujitsu Data Intelligence PaaS.' This allows HR staff to comprehensively utilize the information necessary for evaluating transfer plans, enabling multifaceted analysis.
2. Formulation of Transfer Plans Using Mathematical Optimization Models
TRUSCO Nakayama considers configurations for approximately 100 personnel per transfer cycle, resulting in 10 to the power of 158 possible combinations. The application uses a custom-built mathematical optimization model developed by Fujitsu specifically for this project, taking quantitative criteria such as years of tenure as input conditions. It identifies placement plans that satisfy various conditions from the vast number of combinations, resulting in a 98% reduction in the man-hours required for plan creation.
3. AI-Powered Interactive Decision Support
In addition to TRUSCO Nakayama's HR information, the team organized the judgment perspectives that HR staff have traditionally prioritized. They built an AI chat function that staff can use for reference when creating transfer plans. This feature allows HR staff to interactively verify if the transfer plans generated by the optimization model comprehensively cover all perspectives. This enables HR staff to make final decisions while considering factors like employee career aspirations and the impacts of placement—elements that quantitative data alone cannot fully address—by leveraging insights gained through dialogue with the AI.
Future Outlook
TRUSCO Nakayama will continue to utilize this application to promote strategic HR transfers based on high-speed initial proposals, furthering its HR strategy of continuous growth for individual employees and sustainable business development. Fujitsu plans to further strengthen its co-creation approach through FDEs, promoting projects rapidly while working directly on-site.
Background
TRUSCO Nakayama has been pursuing a talent strategy that promotes individual employee growth and organizational optimization through cross-departmental transfers. In considering these plans, the company has made placement decisions considering both individual growth and total organizational optimization. Furthermore, as part of creating a work environment where employees can feel secure and work for a long time, the company has established diverse personnel systems, which has led to increased complexity in the matters to be considered during transfers. As a result, formulating transfer plans based on these elements was highly time-consuming.
Overview of the Initiative
Given the nature of HR work, which relies heavily on experience, tacit knowledge, and judgment in specific situations, Fujitsu's Forward Deployed Engineers (FDE) embedded themselves on-site. Working alongside TRUSCO Nakayama's HR department, they organized the HR transfer workflow and business processes. Following this, both companies worked on accelerating the HR transfer decision-making process using data and AI, rapidly developing an application that proposes transfer plans accounting for complex factors.
This application features the following three key characteristics, contributing to the efficiency of decision-making in TRUSCO Nakayama's personnel transfers by supporting HR staff in planning and evaluating placement proposals:
1. Centralized Management of Scattered Information
The application centralizes personnel data previously scattered across multiple internal systems and Excel files onto the 'Fujitsu Data Intelligence PaaS.' This allows HR staff to comprehensively utilize the information necessary for evaluating transfer plans, enabling multifaceted analysis.
2. Formulation of Transfer Plans Using Mathematical Optimization Models
TRUSCO Nakayama considers configurations for approximately 100 personnel per transfer cycle, resulting in 10 to the power of 158 possible combinations. The application uses a custom-built mathematical optimization model developed by Fujitsu specifically for this project, taking quantitative criteria such as years of tenure as input conditions. It identifies placement plans that satisfy various conditions from the vast number of combinations, resulting in a 98% reduction in the man-hours required for plan creation.
3. AI-Powered Interactive Decision Support
In addition to TRUSCO Nakayama's HR information, the team organized the judgment perspectives that HR staff have traditionally prioritized. They built an AI chat function that staff can use for reference when creating transfer plans. This feature allows HR staff to interactively verify if the transfer plans generated by the optimization model comprehensively cover all perspectives. This enables HR staff to make final decisions while considering factors like employee career aspirations and the impacts of placement—elements that quantitative data alone cannot fully address—by leveraging insights gained through dialogue with the AI.
Future Outlook
TRUSCO Nakayama will continue to utilize this application to promote strategic HR transfers based on high-speed initial proposals, furthering its HR strategy of continuous growth for individual employees and sustainable business development. Fujitsu plans to further strengthen its co-creation approach through FDEs, promoting projects rapidly while working directly on-site.
FAQ
Why did TRUSCO Nakayama introduce AI into HR operations?
To solve the complexity and time consumption of placement planning due to diverse HR systems, and to promote strategic personnel transfers that balance employee growth with organizational optimization.
How does the mathematical optimization model generate transfer plans?
It takes quantitative criteria—such as tenure, preferred department, skills, and evaluation history—as input conditions to identify the optimal configuration from a vast number of combinations.
What is the division of labor between AI and humans?
AI proposes quantitatively optimized placement plans, while humans verify these plans via interactive chat and make final decisions by considering career aspirations and tacit knowledge.