This project analyzes vendor performance using purchasing, sales, invoice, and inventory data to identify high and low performing vendors and uncover drivers of profitability and turnover.
1. src/01_data_cleaning_and_eda.ipynb
Cleans and explores the datasets, validates joins/keys, and prepares analysis-ready tables.
2. src/02_database_build.ipynb
Builds the consolidated dataset(s) used for vendor-level analysis.
3. src/03_vendor_performance_analysis.ipynb
Performs vendor performance analysis and generates key outputs/insights.
• Raw input datasets are under data/raw/
• Processed / combined datasets are under data/processed/
• Large raw files (if excluded) are documented in data/README.md
Python (pandas, numpy), Jupyter Notebook, and Power BI.
Key outputs include vendor-level performance summaries and insights to support purchasing and inventory decisions.
A Power BI dashboard was created to visualize vendor performance metrics and make insights accessible to non-technical stakeholders.
The dashboard highlights: • Vendor-level sales, purchases, and profitability
• Inventory movement and turnover patterns
• Identification of high- and low-performing vendors
• Comparative views to support purchasing and inventory decisions
Due to file size limitations, the Power BI .pbix file may not be stored directly in this repository. The dashboard was built using the processed datasets generated from the analysis notebooks.