Objective
One of the largest airlines in the world came to our team seeking UX/UI guidance on their new pilot forecasting & demand application that had machine learning (ML) at its core to better predict when pilot demand would arise. This product was developed in the fight against increasing flight delays and cancellations that have greatly impacted the airline industry in recent years. Our client had developed initial high-fidelity wireframes for the product based on initial user research, however, there were key flaws in the user experience that needed to be addressed. Overall the UX/UI of the web application (including core components) was not developed with a human-centered approach, so it needed a complete overhaul. Some key things we noted were the site’s inconsistent component usage across all core screens of the web app, there were also major accessibility issues that needed to be addressed with each component. Another key flaw was interactions, as the current interactions were not developed with the core needs of the user in mind so it made interacting with the application confusing & frustrating.
SOlution
Using my experience and skillset in UX/UI and product design, I got to work taking apart the core features of the product’s 3 main screens and rebuilding them using Carbon Design as an accelerator. I focused specifically on the core user’s journey, what their main goals and needs were, and how we could make completing their main tasks as simple as possible. I also used my knowledge of accessibility to ensure the components of the product were passing key WCAG 2 guidelines by following the rules of P.O.U.R (perceivable, operable, understandable, robust). To further engage our client in thinking about the evolution of this innovative project, I designed a future vision of the application as an AI-driven dashboard that can easily predict discrepancies in pilot demand to ensure all pilots are staffed correctly to decrease flight delays and cancelations.
Role
Lead UX/UI designer
The first screen I re-developed was this template input screen. The main screen the user uploads all pilot data through.
The second screen I re-developed was this template summary screen. This screen allows the users to view all upload data templates and to view/edit them before final submission to the ML model.
The third screen I re-developed was the final screen the user interacts with, which is the data output screen. Once the ML model has digested the data, the application provides the user with various data graphs, that shows the forecasted demand for all pilot data that was inputed into the system for the particular run.
Developing an innovative COGNIZANT Dashboard
In order to go above and beyond for our client, I needed to showcase our expertise in cutting edge product design that uses artificial intelligence (AI) at it’ core. I developed a future vision dashboard to display many of IBM’s market leading capabilities. This one page dashboard envisions our client’s pilot demand forecasting application into a Smart Hub application, where the user can log in to their Smart Hub account, and be directly greeted with their intelligent Smart Hub dashboard. This fully customizable Smart Hub will alert Jeffrey (core user) through N.L.P. (natural language processing) to any discrepancies in upcoming pilot demand, and suggest solutions (based on historical data) so that he can enact the right solution to remedy these discrepancies in pilot demand.
