The Mobile Operations & Maintenance System (MOMS) is utilised by more than 2,000 users across 22 departments to perform routine maintenance work. The current system relies on touch-based manual inputs into tablets. This method is impractical in work situations that involve: tight spaces, the use of both hands, and personal protective equipment. As a result, users have the tendency of filling up the checklist or report after the maintenance has been completed, which might lead to accidental omission of some information.

We are interested in an innovative user interface or data capture methods, such as visual analytics and NLP techniques that could minimise the need to perform touch-based manual inputs. Commonly used maintenance tools, such as measuring callipers and ammeters, could have IoT capabilities to seamlessly capture data. The data captured would be integrated to MOMs to automatically update the checklist. 

The key outcomes of the solution are the reduction in human-input errors and improved timeliness of updates during the maintenance process.