📊 Vendor Performance Analytics Dashboard

An end-to-end Vendor Performance Analytics project that combines SQL, Python, SQLite, and Power BI to analyze vendor sales, purchases, profitability, and inventory performance. The project transforms raw transactional data into interactive business dashboards that help identify vendor risks, profitability trends, and inventory optimization opportunities.

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📷 Dashboard Preview

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📌 Business Problem

Retail and wholesale businesses generate large volumes of purchase and sales data. Without effective analysis, organizations may experience:

• High dependency on a few vendors
• Slow-moving inventory
• Low-performing brands
• Reduced profitability
• Poor purchasing decisions

This project analyzes vendor performance to generate actionable business insights and support data-driven decision making.

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🎯 Objectives

• Analyze vendor sales and purchase performance.
• Identify top-performing vendors and brands.
• Measure profitability using Gross Profit and Profit Margin.
• Detect slow-moving inventory and unsold capital.
• Identify high-risk vendors using Vendor Risk Ranking.
• Build an interactive Power BI dashboard for business users.

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🛠 Tech Stack

• Python
• SQL
• SQLite
• Pandas
• Power BI
• Jupyter Notebook

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📂 Project Structure

vendor-performance-analysis
│
├── data
│   └── vendor_sales_summary.csv
│
├── notebooks
│   ├── Exploratory Data Analysis.ipynb
│   └── Vendor Performance Analysis.ipynb
│
├── scripts
│   ├── ingestion_db.py
│   └── get_vendor_summary.py
│
├── powerBI
│   └── vendor_performance.pbix
│
├── reports
│   └── Vendor Performance Report.pdf
│
├── images
│   └── dashboard.png
│
├── requirements.txt
├── .gitignore
└── README.md

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🔄 Project Workflow

Raw CSV Data
      │
      ▼
SQLite Database
      │
      ▼
SQL Data Aggregation
      │
      ▼
Python Data Cleaning & Feature Engineering
      │
      ▼
Exploratory Data Analysis
      │
      ▼
Power BI Dashboard
      │
      ▼
Business Insights & Recommendations

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📈 Dashboard KPIs

The dashboard provides key business metrics including:

• 💰 Total Sales
• 💳 Total Purchase
• 📈 Gross Profit
• 📊 Profit Margin
• 📦 Unsold Inventory Capital
• 🏆 Top Vendors by Sales
• 🥃 Top Brands by Sales
• 🍩 Vendor Purchase Contribution
• ⚠️ Top High-Risk Vendors
• 📉 Low Performing Brands

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✨ Dashboard Enhancement

To improve business decision-making, I extended the dashboard by introducing a Top High-Risk Vendors visualization.

The Vendor Risk Ranking helps identify vendors that require closer monitoring based on operational risk indicators, enabling quicker prioritization and inventory management decisions.

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📊 Key Business Insights

• The top vendors contribute a significant share of total purchases, indicating vendor dependency.
• Several brands exhibit low sales despite maintaining healthy profit margins.
• Inventory worth $2.71M remains unsold, highlighting opportunities for stock optimization.
• Vendor Risk Ranking helps prioritize vendors requiring operational attention.
• Interactive dashboards enable quick exploration of sales, purchases, and profitability trends.

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🚀 Skills Demonstrated

• SQL Joins & Aggregations
• Data Cleaning
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Business Intelligence
• Power BI Dashboard Development
• KPI Design
• Data Visualization
• Business Insight Generation

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📌 Future Improvements

• Add dynamic slicers for vendor and brand analysis.
• Build forecasting models for sales and inventory.
• Deploy the dashboard using Power BI Service.
• Integrate live database connectivity.
• Add predictive analytics for vendor performance.

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👩‍💻 Author

Nishika Roy

Electronics & Communication Engineering
Indian Institute of Information Technology (IIIT) Kota

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