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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.

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

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

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

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