Vendor Performance Analysis

This project focuses on evaluating vendor performance through data-driven insights. It involves cleaning and analyzing procurement datasets, identifying top-performing vendors, and visualizing key metrics such as purchase contribution, order frequency, cost efficiency, and inventory trends. The insights generated support strategic procurement decisions and business intelligence initiatives.

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Technologies Used

• Python: Data cleaning, EDA, predictive modeling
• SQLite: Database management
• Power BI: Interactive dashboards and visualizations
• Excel: Data preprocessing and analysis
• Figma: Dashboard prototyping
• Git/GitHub: Version control

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Features

• Conduct Exploratory Data Analysis (EDA) on vendor, purchase, and sales datasets
• Identify key risk indicators, such as missed payments and high credit utilization
• Develop interactive dashboards for vendor performance, sales trends, and inventory levels
• Generate actionable insights for procurement optimization and cost reduction
• Enable real-time reporting via Power BI Service

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Dataset

The dataset

Files included:

• purchases.csv
• sales.csv
• begin_inventory.csv
• end_inventory.csv
• purchase_prices.csv
• vendor_invoice.csv
• vendor_sales_summary.csv

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Project Structure

Vendor-Performance-Analysis/
│
├── data/                  # Folder for datasets (gitignored)
├── logs/                  # Log files
├── scripts/               # Python scripts
│   ├── get_vendor_summary.py
│   └── ingestion_db.py
├── .gitignore             # Ignored files
├── EDA.ipynb              # Exploratory Data Analysis notebook
├── Untitled.ipynb         # Additional notebook
├── vendor_Performance.pbix # Power BI dashboard
├── inventory.db           # SQLite database
└── README.md              # Project documentation