An end-to-end data analytics project that evaluates vendor efficiency, purchasing strategies, inventory turnover, and profitability using SQL, Python, and Power BI to support data-driven retail business decisions.
Vendor performance plays a crucial role in maintaining a profitable and efficient retail supply chain. Businesses must continuously monitor vendor contributions, purchasing costs, inventory movement, and profit margins to make informed procurement decisions.
This project performs an end-to-end Vendor Performance Analysis using retail sales, inventory, and purchasing data. The complete analytical pipeline includes SQL-based ETL, Python for data cleaning, exploratory data analysis (EDA), statistical testing, and Power BI dashboards for interactive business reporting.
The objective is to uncover purchasing trends, identify underperforming vendors and brands, optimize inventory management, and provide actionable business recommendations.
This project aims to answer important business questions related to vendor performance and inventory management, including:
- 📦 Which vendors contribute the highest sales and profits?
- 💰 Does bulk purchasing significantly reduce procurement costs?
- 📉 Which brands require promotional or pricing adjustments?
- 🏪 How much inventory remains unsold?
- 📊 Which vendors generate the highest profit margins?
- 📈 Are there statistically significant differences in vendor profitability?
⚠️ Is the business overly dependent on a small number of vendors?
This project investigates several business questions that help procurement teams and retail managers improve operational efficiency.
| Business Question | Objective |
|---|---|
| 🏢 Which vendors contribute the most purchases? | Identify major vendors driving procurement. |
| 💰 Does buying in bulk reduce procurement costs? | Measure cost savings achieved through large purchase quantities. |
| 📦 Which brands require promotional strategies? | Detect brands with low sales but strong profit margins. |
| 📉 How much inventory remains unsold? | Identify slow-moving inventory affecting profitability. |
| 📊 Which vendors deliver the highest profit margins? | Compare vendor-level profitability. |
| 📈 Are vendor profit margins statistically different? | Validate differences using hypothesis testing. |
| Measure vendor dependency and business risk. |
By completing this analysis, the project aims to:
- Improve vendor selection strategies.
- Optimize procurement decisions.
- Reduce inventory holding costs.
- Identify opportunities for promotional campaigns.
- Detect procurement risks due to vendor dependency.
- Demonstrate practical SQL, Python, and Power BI skills through a real-world business case.
- 📊 Built a complete retail analytics pipeline using SQL, Python, and Power BI.
- 🧹 Performed extensive data cleaning and preprocessing.
- 🗄️ Designed SQL queries for ETL and vendor summary generation.
- 📈 Conducted Exploratory Data Analysis (EDA).
- 📉 Performed correlation analysis between business variables.
- 🧪 Applied statistical hypothesis testing using SciPy.
- 📊 Built an interactive Power BI dashboard for business reporting.
- 💡 Generated actionable recommendations for purchasing and inventory optimization.
- SQL
- Python
- Pandas
- Matplotlib
- Seaborn
- SciPy
- Power BI
- Jupyter Notebook
- Git & GitHub
vendor-performance-analysis/
│
├── 📁 dashboard/
│ └── vendor_performance.pbix
│
├── 📁 images/
│ ├── Dashboard.png
│ └── pipeline.png
│
├── 📁 notebooks/
│ ├── Exploratory Data Analysis.ipynb
│ └── Vendor Performance Analysis.ipynb
│
├── 📁 scripts/
│ ├── ingestion_db.py
│ └── get_vendor_summary.py
│
├── 📄 LICENSE
├── 📄 README.md
└── 📄 Vendor Performance Report.pdf
The project uses multiple retail datasets containing purchasing, inventory, and sales information.
The datasets include information such as:
- Vendor Details
- Purchase Transactions
- Sales Transactions
- Inventory Levels
- Purchase Quantity
- Sales Quantity
- Freight Costs
- Gross Profit
- Profit Margin
- Purchase Price
- Sales Price
After ingestion, a consolidated Vendor Summary Table is generated for analysis.
The following preprocessing steps were performed:
- Removed transactions where:
- Gross Profit ≤ 0
- Profit Margin ≤ 0
- Sales Quantity = 0
- Converted columns into appropriate data types.
- Merged multiple datasets.
- Created vendor-level summary tables.
- Detected and handled outliers.
- Validated missing and inconsistent values.
The analysis focused on understanding retail purchasing and inventory behavior.
- Vendor contribution analysis
- Brand performance analysis
- Inventory turnover analysis
- Bulk purchasing analysis
- Profitability analysis
- Freight cost analysis
- Correlation analysis
- Outlier detection
- Statistical hypothesis testing
The project includes various visualizations such as:
- 📊 Bar Charts
- 📈 Line Charts
- 📉 Scatter Plots
- 📦 Box Plots
- 🔥 Correlation Heatmaps
- 📊 Vendor Performance Dashboard (Power BI)
- The Top 10 vendors account for 65.69% of total purchases, indicating a high dependency on a small group of suppliers.
- Bulk purchasing reduces procurement cost by approximately 72% per unit, demonstrating significant cost-saving opportunities.
- Around $2.71 Million worth of inventory remains unsold, highlighting slow-moving stock and inventory management challenges.
- 198 brands have low sales but high profit margins, making them strong candidates for promotional campaigns.
- Purchase Quantity and Sales Quantity show an almost perfect positive correlation (0.999).
- Purchase Price has a weak relationship with overall profit.
- Profit Margin has a slight negative relationship with Sales Price.
Statistical analysis confirmed a significant difference in vendor profit margins, suggesting distinct pricing and purchasing strategies among vendor groups.
The interactive Power BI dashboard provides insights into:
- Vendor-wise Sales
- Vendor Profitability
- Inventory Turnover
- Bulk Purchase Savings
- Purchase Distribution
- Performance Heatmaps
- Procurement KPIs
git clone https://github.com/yourusername/vendor-performance-analysis.gitpip install -r requirements.txtpython scripts/ingestion_db.pypython scripts/get_vendor_summary.pyjupyter notebookOpen:
notebooks/exploratory_data_analysis.ipynb
and
notebooks/vendor_performance_analysis.ipynb
Finally, open:
dashboard/vendor_performance_dashboard.pbix
to explore the interactive dashboard.
The datasets used in this project are not included in this repository due to sharing restrictions.
If you'd like to reproduce the analysis, explore the Power BI dashboard, or use this project for learning purposes, feel free to reach out to me. I'll be happy to share the dataset (where applicable) and guide you through the project setup.
📧 Email: latikar109@gmail.com
💼 LinkedIn: https://www.linkedin.com/in/latika-ray
Note: The repository includes the complete analysis pipeline, notebooks, scripts, and dashboard. Only the raw data files are excluded.
- SQL ETL
- Data Cleaning
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Business Analytics
- Data Visualization
- Correlation Analysis
- Hypothesis Testing
- Statistical Analysis
- Power BI Dashboard Development
- Inventory Analytics
- Procurement Analytics
- Business Insight Generation
Based on the analysis, the following recommendations are proposed:
- Diversify the vendor base to reduce procurement risk.
- Increase bulk purchasing where cost savings are significant.
- Promote high-margin brands with low sales.
- Develop strategies to clear slow-moving inventory.
- Continuously monitor vendor performance using interactive dashboards.
- Optimize purchasing strategies based on vendor profitability.
Latika Manoj Ray
Aspiring Data Analyst | Python | SQL | Power BI | Data Visualization | Machine Learning
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