Graduation Project · Bursa Uludağ University × Oyak Renault
Yeni Araç Projesi (New Vehicle Project)
Digital Raw Material Cost Management & Productivity Improvement
RMApp, a dashboard we built to digitalise per-vehicle raw material cost tracking on a production line, based on Pareto analysis and comparative price tracking.
About the project
This was my graduation project in Industrial Engineering at Bursa Uludağ University, carried out in collaboration with Oyak Renault. Our focus was to move per-vehicle raw material cost tracking on a production line into a digital environment and make the process more efficient.
I worked on the project with İrem Aktaş and Beril Arda as a team of three, with support from Onur Furkan Kaya and Samet Açıkgöz throughout.
Goal and starting point
Per-vehicle raw material costs were being tracked manually. Compiling and updating the data by hand could be time-consuming and made it harder to see cost changes as a whole.
The goal was to digitalise this tracking, identify the critical raw materials and build a structure for comparing costs across different price sources, making data-driven decisions easier.
My role and responsibilities
As a member of a three-person team, I worked with my teammates across the different stages of the project:
- Reviewing the existing cost tracking process and defining requirements
- Preparing the data for analysis
- Identifying critical raw materials with Pareto analysis
- Building a structure for comparative cost tracking across price sources
- Preparing the RMApp dashboard and reporting the results
I used Python at a beginner level in this project, together with Pandas, Streamlit and Plotly, to organise and visualise data. The real focus was not advanced software development, but making cost tracking more structured and easier to follow.
Technologies and methods
Python
Core language of the app
Pandas
Cleaning and combining data
Streamlit
Interactive dashboard interface
Plotly
Interactive charts
Excel
Data sources and initial analyses
Process
- 01
Reviewing the current state
We looked at how per-vehicle raw material costs were tracked and which data was used.
- 02
Preparing the data
We brought data from different sources into a consistent structure that could be analysed.
- 03
Identifying critical materials
Using Pareto analysis, we identified the raw materials with the largest share of total cost and clarified tracking priorities.
- 04
Comparative cost structure
We built a structure that lets raw material costs from different price sources be followed side by side.
- 05
The RMApp dashboard
We developed RMApp, a dashboard built with Python, Streamlit, Pandas and Plotly for exploring costs interactively.
Outcomes and learnings
What was built
- A digital solution, accessible from a single interface, was created for what had been a manual cost tracking process.
- The critical raw materials identified through Pareto analysis provided a basis for focusing tracking efforts on priority items.
- The structure for comparing costs across price sources was designed to support data-driven decision making.
Personal takeaways
- Working through a real manufacturing problem from start to finish
- Applying core industrial engineering tools such as Pareto analysis to a business problem
- Learning and using Python, Pandas, Streamlit and Plotly on a real project
- Sharing work within a team and communicating with company stakeholders
Visuals
- 1Manual tracking
- 2Data preparation
- 3Pareto analysis
- 4Cost comparison
- 5RMApp dashboard
As this project was carried out in collaboration with a company, real data, prices, supplier information and screenshots are not shared.