Brighton Science

Brighton Science is a well established science company that makes a device used in the automotive and aerospace industry.

They are in need of a new page on their B-Connect internal app in order to compile results of a specific test called the GR&R. Their needs are straightforward. This page enables users to see if the test has passed or failed, and why.

Background

The bulk of the work in this project was to understand the complexities of it. What is a Surface Analyst? What is a GR&R? How does a test pass or fail? What do the results mean? And what is the data the client wants to see in their new feature?

Brighton Science is a Minnesotan company that produces a device that analyses surfaces in order to see the if they are ready or not to paint. Their main markets are the aerospace and the automotive industry. Their device is called a Surface Analyst. In order to be used, it needs to be tested. This test is called a GR&R (Gage Repeatability & Reproducibility) and the same series of measurements is performed by three different technicians (or appraisers) and is analyzed by the QA Manager. If there is more than 30% variation, then the test has failed. However there can be different reasons for the test to fail. Including an unclean surface, a technician error, a lack of training, or a Surface Analyst that does not work. The reasons are shown by the results. At the moment, the GR&R results are hand compiled in a spreadsheet.

Brighton Science has an app called the B-Connect. It is very usable and affords much clarity. However it does not have a clear result page. My design team was charged with coming up with solutions to their problem. Their design system is established, simple, practical, and science focussed.

Methodologies

  • Stakeholder interview

  • Journey mapping

  • Feature concepts

  • Kano Analysis

  • Feature scoping with Agile programing team

  • High fidelity annotated wireframing

Solutions

Once again, the complexity at bay was to understand what is a GR&R, what the results could possibly mean, and what Brighton Science wanted us to do.

Once I did my research, understanding the process was very easy. I started by doing a GR&R journey map, highlighting the areas the result page would have to compile.

I then sketched some concepts. Four out of five of my concept sketches were scoped by the Brighton Science team of programmers working in an Agile framework. The first one did not make sense as there was a misunderstanding on the information the main user would need to access. However the other three were approved as a project of medium difficulty within Scrum.

My concepts were put through a Kano Analysis. They made sense.

After further synthesis of information, I made a sequence of high fidelity wireframes in Figma.

I put together all my information in the following project proposal with annotated wireframes. This is my final deliverable.

Screenshots from the Project Proposal with the Annotated High Fidelity Prototype, by Chloe Dalbard

Outcome

I made a sequence of high fidelity prototypes following Brighton Science’s design system coming up with design ideas for a result page of the GR&R test within their B-Connect app. As a result, users will not have to compile the information coming from that test by hand inside Excel spreadsheets anymore. This will be a massive time saver for both the managers and the technicians. This work was passed on to the development team.

My work in this projects had mainly to do with understanding the complexity of the problem. My low-fidelity prototypes were he ones that were selected by the design team. These are the ones I prototyped.