This project analyzes vendor sales and inventory performance using exploratory data analysis (EDA) to optimize business decisions in retail/wholesale environments.
The key objective is to uncover insights that help enhance profitability, streamline inventory, and evaluate vendor efficiency.
- Identify underperforming brands that could benefit from price adjustments or promotions.
- Determine top vendors contributing most to revenue and gross profit.
- Evaluate the impact of bulk purchasing on cost efficiency.
- Analyze inventory turnover to reduce storage costs and increase cash flow.
- Investigate profitability variance between high-performing and low-performing vendors.
- Profit Margins & Sales: 198 brands showed high margins but low sales — ideal for targeted marketing.
- Vendor Dependency: 65.7% of purchases came from the top 10 vendors — a potential supply chain risk.
- Bulk Purchase Advantage: Buying in large quantities reduced per-unit costs by up to 72%.
- Inventory Inefficiencies: $2.71M worth of inventory remained unsold due to low turnover.
- Profitability Models: Statistical tests confirm significantly different profit strategies between vendor groups.
- Reprice low-selling, high-margin products to stimulate demand.
- Diversify vendors to avoid over-reliance and reduce risk exposure.
- Use bulk pricing strategically to maintain profitability.
- Clear slow-moving stock and avoid over-purchasing.
- Boost marketing and reach for vendors with limited sales but high profit margins.
| File Name | Description |
|---|---|
Vendor Performance Report.pdf |
Full analysis report with insights, visualizations, and recommendations |
inventory.db |
📎 Download via Google Drive |
raw_sales_data.csv |
📎 Download via Google Drive |
- Python: For data cleaning and analysis
- Pandas / NumPy: Data manipulation
- Matplotlib / Seaborn: Visualizations in EDA
- SQLite: Data storage (
inventory.db) - Power BI: 📊 Interactive dashboards and visual summaries
- Jupyter Notebook: For step-by-step analysis