Inspiration
As data science majors, we are passionate about leveraging data to solve real-world problems such as QRV electric vehicles. Through applying data science skills to optimize a Bill of Materials, we aim to assist in achieving a seamless product development process, ultimately advancing sustainable transportation solutions for the future. In addition, this challenge provides us the opportunity to practice data science preprocessing skills in a realistic problem.
What it does
Report on errors within the BoM and analysis on errors.
How we built it
We used Python and SQL
Challenges we ran into
When constructing our tree data structure, we created many Python scripts and functions to assist in its construction. When attempting to build our tree, we quickly found that the runtime was O(N^3) resulting in wasted hours waiting for our code. Recognizing this limitation, we worked to reduce the runtime to O(N^2) allowing for the construction of our tree to be much faster.
Accomplishments that we're proud of
Overcoming most of the challenges we faced and putting together a presentation on it so that others without DS backgrounds can easily understand.
What we learned
How to use Deepnote, practiced Python and SQL in realistic problem.
What's next for Quick Release Automotive BoM Analysis
To avoid issues in the Bill of Materials, we can Utilize and enforce Database constraints to guarantee that logic errors are avoided. For example, because all parts custom-designed by a company and standard components must have a procurement code, we can enforce that employees editing the Bill of Materials must include a Procurement Code and can not remove Procurement Codes without removing the whole entry.
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