Mcrew for GE
When we think of logistics our minds naturally gravitate to companies such as UPS or FedEx distributing packages from retailers to consumers. However, there is a vast and almost entirely remote infrastructure of railroads that we rarely see. This infrastructure serves as a connector from port cities to transcontinental railroads and interchanges with minuscule to enormous manufacturers. Logistics is the railroad’s game of Tetris dealing with passage rights, waybills, hazardous materials, and complex billing. To be a key player in this game you need a tool that levels the playing field.
GE developed a tool to take on railway logistics from the crew’s perspective, mCrew. It used a ruggedized Windows platform that connected and synchronized a crew’s movements with the centralized train management system (TMS). This was effectively a game changer, however over time the ruggedized Windows platform has become less and less reliable and will soon no longer be a viable option.
With the impending change, I was tasked with advancing mCrew’s functionality via Predix GO, an insanely powerful tool to organize and recall assets and their attributes for all mobile devices. Predix GO’s UI operates as the entry point from an asset to HTML5 cards, where work is completed. The goal was to create a series of cards to mimic the actions of mCrew as it existed but remove the pain-points and simplify interactions.
To do this I needed to understand who the crew was and how they worked. After visiting two railroads and working with a variety of crews two main personas of a yard crew and train crew were defined. The characteristics of work between these personas was similar, but each crew had a unique vernacular in for actions and descriptions of the movements between track, train, and customer. Also, the crew’s input of work was more reminiscent of data entry than task completion, literally reading the tasks then entering movement data into the device. Not a compelling way to do any work.
From these findings, I was able to craft a very simple information architecture that utilized Predix Go’s task management UI as an entry point to two cards where the movement work would be completed. The information architecture was developed into a Predix Go / Node Stack prototype to then test with the users for feedback.
The feedback identified the need for a third card existing between the Predix Go’s task management UI and the initial cards. This card expanded the detail of the tasks while allowing rapid movement completion, and providing the ability for the user to further drill into a specific tasks and act within a variety of views for any adhoc work.
The final result (TBD) will be a lightweight app that gives crews tasks to complete with out having to duplicate information from a system generated paper list to then enter its completion back into a system. The app will dramatically speed up systemwide documentation, while returning data for consumption so the crew can just focus on railcar movement.
User Experience Design
User Interface Direction