Japanese manufacturing technology publication MONOist reported on Kawasaki Heavy Industries’ (KHI) “AI-Ready Maintenance” concept using mapxus Driven by Kawasaki™. KHI presented the concept at the 2nd Smart Maintenance Expo Tokyo, part of Manufacturing World Tokyo, held from July 1 to 3, 2026, at Tokyo Big Sight.
The concept focuses on preparing maintenance and manufacturing data for future AI use by adding spatial context to existing site records. In the demonstration, KHI showed how indoor and outdoor location information could be brought together with work records, communication records, and issue histories to create a more connected view of on-site operations.
MONOist explains that mapxus uses Wi-Fi-based positioning to obtain indoor location information in areas where GPS is difficult to use. With smartphones or IoT devices, it can display the locations of people and equipment on indoor maps and connect with other applications through an SDK. KHI also demonstrated the use of GPS alongside indoor positioning to extend location context across indoor and outdoor environments.
Adding spatial context to maintenance data
Maintenance inspection records and manufacturing-site data can capture results, numerical values, and completed checks. However, they may not capture where work took place, what actions were performed, or the surrounding operational context.
According to the KHI representative quoted by MONOist, recording this context could help convert tacit knowledge into explicit knowledge that can be shared across teams. The resulting data could also be used for AI analysis and improvement.
Use cases for operations and future automation
At the exhibition, KHI presented several potential use cases. During an emergency such as a fire, location information could support instructions tailored to workers in different parts of a site. During normal operations, teams could use location and work progress information to monitor inspections and coordinate assistance when tasks are delayed.
KHI also explained that the accumulated data could support future robotics applications by helping robots learn the considerations involved in work previously performed by people.
The MONOist coverage highlights how indoor location information can contribute to more than location visibility. By connecting work records with spatial context, it can help organizations build a stronger data foundation for operational analysis, knowledge transfer, and future AI applications.
Read the original MONOist article in Japanese:
川崎重工が「AI-Readyな保全」提案、屋内位置情報で現場作業を丸ごと可視化
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