{ "imageCollage": "https://cdn.builder.io/api/v1/image/assets%2Ffb3ccc876dd442c6ae31d776377e35db%2F42da6f89e14a46bcba19bd112d002698", "featuredImage": "https://cdn.builder.io/api/v1/image/assets%2Ffb3ccc876dd442c6ae31d776377e35db%2F574cbb4798f6420aba966e6a679a5a31", "qoute": "\"I'm most grateful that you were able to manage communications with stakeholders in the United States.\"", "industry": "Consumer Electronics and Machinery", "qoutesBlock": {}, "clientName": "Kubota", "site": "jp_en", "relatedLinks": {}, "publishedDate": 1743660000000, "seo": { "title": "Kubota | Kubota Diagnostics: Diagnostic Application | Monstarlab", "description": "Kubota is a global company that provides total solutions using construction and agricultural machinery and other products that meet the user needs of various regions around the world. The Monstar Lab Group participated in the development project of \"Kubota Diagnostics,\" a 3D model and AR-based fault diagnosis application provided by the company for service engineers at their sales distributors, from the requirements definition phase. We were responsible for UI design, design, and product development." }, "headerBanner": "https://cdn.builder.io/api/v1/image/assets%2Ffb3ccc876dd442c6ae31d776377e35db%2F574cbb4798f6420aba966e6a679a5a31", "overview": "Kubota is a global company that provides total solutions using construction and agricultural machinery and other products that meet the user needs of various regions around the world. The Monstar Lab Group participated in the development project of \"Kubota Diagnostics,\" a 3D model and AR-based fault diagnosis application provided by the company for service engineers at their sales distributors, from the requirements definition phase. We were responsible for UI design, design, and product development.", "content2": "

Results:

At the meeting for overseas sales distributors where this application was announced prior to its release, significant attention was drawn to the mechanism for visually diagnosing faults using 3D models and AR, along with the UI design that offers excellent operability. Furthermore, expectations are high for its future contribution in terms of service engineer training and staffing.

In the future, this application is scheduled to be sequentially rolled out to markets around the world, including Japan, and at the same time, the expansion of target equipment based on this application is being considered.

Monstar Lab will continue to provide localization support to various parts of the world, aiming to further improve the accuracy of the fault diagnosis flow and expand its functionality.

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Challenges:

Kubota, a global company that provides total solutions using construction and agricultural machinery and other products that meet the user needs of various regions around the world, has established many overseas sales subsidiaries. However, most construction machinery repairs are handled by service engineers at local sales distributors, and depending on the experience and skills of the person in charge, manuals alone may not provide sufficient support.

Decreased operating rates of construction machinery due to downtime directly lead to reduced user revenue. Therefore, there was a need for fault diagnosis support that was rapid, efficient, easy to understand for anyone, and not dependent on the abilities of service engineers.

Also, since the demand for fault diagnosis was highest in the United States, the scope for operational launch and effectiveness verification was set for the U.S. market. With an eye toward subsequent global expansion, there was a need for a product development partner with expertise in the global market.

Solutions:

In conventional fault repair, it was necessary to search through the vast amount of information in the construction equipment manual to find the relevant location, and simply locating the necessary information took a significant amount of time. As a solution to improve time efficiency, we built a simple fault diagnosis flow that automatically displays inspection points and repair methods by inputting the error codes or malfunction symptoms emitted by the machine. This made it possible to quickly identify the faulty location without relying on the knowledge and experience of service engineers.

Furthermore, by combining 3D models and AR, we incorporated a function that allows users to visually recognize the faulty location inside the construction equipment and identify target parts by holding up a smartphone. By eliminating the need to actually inspect the inside of the construction equipment, we enabled efficient repairs.

For UI design and architecture, we assigned native designers with extensive experience in global projects from Monstar Lab Group's A.C.O. (now integrated into Monstar Lab). By incorporating trends for U.S. users and restructuring the Japanese manual for English-speaking regions, we focused on creating user-friendly touchpoints for local users. At the same time, we also contributed as a bridge for communication with Kubota's local staff.

In terms of user touchpoints, we incorporated UI design that considered use on construction sites, such as using large, highly readable fonts, employing large buttons that are easy to tap with one hand, and using pictograms for quick comprehension.

On the development side, we adopted Flutter, which allows for multi-device compatibility, considering scalability. We built a proprietary CMS and accumulated logs and user feedback, enabling us to gather various information that leads to future improvements in the diagnostic flow and fault prediction.

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