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When I joined Gartner as a UX consultant, it's India Center of Excellence team—and Gartner Consulting as a whole—was barely beginning to wet it's toes in digital prototyping and user experience design. There were hardly any pre-defined practices or processes available. My task as their new subject matter expert was to setup what was to become the UX Lab at the Gartner India Consulting division. With a Gartner Symposium around the corner, the expectations out of this new-found practice simply quadrupled.
With the deadline looming just a couple weeks away from my orientation we knew it for a fact that it was going to be a tough challenge to negotiate to deliver the project within the scheduled deadline. Addressing this caveat early helped us identify the feasibile solution and conjure the appropriate plan to achieve it. With insights from recent digital transformation projects in the manufacturing domain coupled with Gartner research papers and hype cycle, we arrived at the problem statement to begin with. In order to keep the context understandable for the audience outside the niche, we decided to design the prototype for addressing the affairs at an automotive assembly plant.
The objective of the medium fidelity prototype was simple—a concept mobile application for floor managers to monitor and preemptively identify impending issues in assembly units, and initiate and track maintenance tickets towards the same. In order to serve the context better, we consulted subject matter experts who gave us overview of assembly floor plans, equipments and sensors respectively.
Based on the knowledge gathered, we built journey map, defined the information architecture and drafted paper-pen sketches via brainstorming session. Upon iteration and refinements, we reached a common consensus about the prototype. With a week to go, I turned the paper-pen sketches to wireframes and UI mockups respectively, gathering feedback and tweaking the design over and over. I finished and implemented the medium fidelity prototype in InVision with a couple days to spare after testing and final review, for Gartner Symposium.
While the project initially caught me off guard as I joined Gartner, it was an experience that has better tailored me to handle pressure-cooker situations with a calm mindset. As for the prototype, it was an immense success at the Symposium where it was presented in over eighty individual sessions. The prototype was greatly appreciated by the attendees, and paved way for the various prototyping engagements that followed.
The application has been designed in such a fashion as to replicate real world scenario in order to imply as much reality in the prototype as possible to ensure that the users feel in control of the context. Since thhe application corresponds to a restricted workspace, there is no option for the user to explicitly sign up unless the administrator avails them access to the system. While simplicity has been maintained throughout the appication to make it self intuitive, there is also a quick tour for the users to quickly get on board with the application.
As the floor managers sign in and select the particular factory floor they wish to monitor, they are greeted with the assembly line of that factory floor. This brings them quickly into their realm and eases the complexity of understanding the application by bringing familiarity into the ecosystem. Since assembly floor are large unified working environments sampled into modules, the appropriate approach to address this model was with accordion cards—cards that expand to reveal contents as the users act upon them.
As the floor manager selects any unit from the assembly floor chart above, they see the robots operating under that assembly unit. If the unit in a
CAUTION status, the card presenting the robot in the said state is displayed first followed by the conventional sequence. The status details of maintenance tickets associated with any robot are abstracted unless the floor manager explicitly explores them further.
The information pertaining to the sensor reading anomalous data is presented upfront, with remedial measures generated based on predictive analytics. The overview of sensor statistics is available in the following card which the floor manager can explore in further detail to view information associated with that sensor parameter.
Predictive analytics drive service request suggestions based on past issue and solution patterns. Service request tickets are pre-populated with information pertaining to the issue and the suggested solution for the floor manager to review and submit for requisite action.
When the user signs in for the first time, they are greeted with on-screen tool-tip tutorials that ease their learning curve by cutting off the cognitive effort of understanding the nuances of the application. This aids them jumpstart directly as a professional instead of having to bid time for learning.
...while you're still here
how about some more…