The Vendor Performance Analysis Dashboard is an end-to-end data analytics project developed using Power BI.
It provides key insights into vendor performance, procurement efficiency, and business spend analysis, helping organizations make data-driven supplier management decisions.
This project demonstrates strong data analysis, ETL (Extract-Transform-Load), and visualization skills using Power BI, Excel, and SQL concepts.
- Evaluate vendor performance based on KPIs such as delivery timeliness, cost efficiency, and quality ratings.
- Identify top-performing and underperforming vendors to support procurement decisions.
- Optimize inventory and purchasing strategies using data insights.
- Present an interactive Power BI dashboard for business stakeholders.
The Power BI dashboard includes:
- 📦 Vendor Performance Overview – On-time delivery, quality, and cost indicators.
- 💰 Spend Analysis – Total purchase cost and vendor-wise expense distribution.
- ⏰ Delivery Timeliness – Comparison of planned vs actual delivery dates.
- 📈 Trend Analysis – Monthly purchase and sales trends.
- 🧾 Key Metrics Cards – Total vendors, total purchases, average cost per order, and defect rates.
| Category | Tools Used |
|---|---|
| Data Visualization | Power BI |
| Data Processing | Excel / CSV Files |
| Database | SQLite (inventry.db) |
| Version Control | Git & GitHub |
| Scripting (optional) | Python / Pandas (for data cleaning) |
Vendor-Performance-Analysis-DA_Project/ │ ├── data/ │ ├── Vendor performance_analysis.pbix # Power BI dashboard file │ ├── data/ │ │ ├── purchases.csv # Purchase transaction data │ │ ├── sales.csv # Sales transaction data │ ├── inventry.db # Inventory database │ ├── .gitignore # Ignored large/system files └── README.md # Project documentation
| Dataset | Description |
|---|---|
| purchases.csv | Contains purchase orders, cost, vendor IDs, and dates. |
| sales.csv | Sales data used to correlate vendor supply with sales impact. |
| inventry.db | SQLite database containing stock and vendor details. |
- Handling and transforming large CSV datasets.
- Designing clean and interactive Power BI dashboards.
- Applying DAX formulas for KPI calculations.
- Version-controlling Power BI and data analysis projects using Git & GitHub.
(Add a screenshot of your Power BI dashboard here)
Example:
- Automate data refresh from SQL database to Power BI service.
- Add supplier segmentation and performance forecasting using Python or Power BI AI visuals.
- Integrate alert systems for underperforming vendors.