Quick Release Pit Stop

Invalid parts by system
Invalid parts by subsystem
Invalid parts by individual parts
Invalid parts by procurement and subcomponent codes
Invalid parts by responsible engineers
Invalid parts by variant car size

Inspiration

We love electric vehicles and want to support their manufacturers. We also were interested in how engineering and manufacturing processes work together.

How we built it

We used pandas for data analysis and plot.ly and seaborn for data visualization. We made bar plots that showed invalid part data by several different groups. With these plots, we recommended several changes for Quick Release to improve production.

Challenges we ran into

The first challenge we ran into was understanding the data and what to look for. There were tens of thousands of lines of code which felt really overwhelming at first. After getting a grasp on the goal of the project, the next hurdle became working with the data which we first tried using google sheets to represent and modify the rows and columns. However this was too slow as we weren’t proficient in managing a spreadsheet, so we switched to deep note and used python along with pandas, seaborn, and matplotlib to manipulate the data. Organizing the data proved to be a challenge with some parts having parents, others not, some with nonexistent parents, etc. A final issue we had come across was how to actually have an effective way to represent the data in forms of visual.

Accomplishments that we're proud of

We’re proud of creating a Node tree to represent each part in the data set. We struggled for hours on conceptually wrapping our heads on how to implement the tree, but we made it through. Each node had a parent and children that we used to validate procurement and subcomponent codes.