Supply-Chain-Vendor-Analytics-Project

This project analyzes vendor performance using a large dataset (1.8+ GB) comprising sales, purchases, inventory, and pricing data. The goal is to generate actionable insights and build dashboards to support data-driven decisions on vendor management and supply chain optimization.

Report

Download Vendor Performance Report

📊 Power BI Dashboard

🔗 View Power BI Dashboard (If link not working, download pbix file)

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📁 Files Used

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

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🔧 Tools & Technologies

• Python (Pandas, NumPy, Seaborn, Matplotlib)
• SQL (joins, aggregations)
• Power BI (visualizations, dashboards)
• Jupyter Notebook

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📊 Key Tasks Performed

• Merged and cleaned 6 large CSV datasets using SQL and Python
• Built a single aggregated vendor performance table name 📄 View full vendor_data_final.csv
• Performed exploratory data analysis (EDA) on profit margins, turnover rates, and sales trends
• Identified low-performing vendors and product inefficiencies
• Created interactive Power BI dashboards for stakeholder reporting
• Delivered actionable business recommendations in a final report

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📈 Dashboards & Insights

Power BI dashboard includes:

• Vendor-wise profit margins
• Sales volume trends
• Inventory turnover rates
• Vendor efficiency metrics