📦 Vendor Performance & Inventory Analytics

📌 Project Overview

This project focuses on analyzing vendor performance and inventory efficiency using transactional sales data.
The objective is to identify profitability drivers, high-risk vendors, and low-rotation inventory to support data-driven business decisions.

The workflow combines MySQL for data processing, Python for analysis, and Power BI for visualization., starting from raw data ingestion into a relational database and ending with dashboard-ready insights.

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🎯 Business Problems

• Which vendors generate high profit but low sales (growth opportunities)?
• Which vendors drive high sales but low margins (pricing or cost risk)?
• How much capital is locked in unsold inventory, and who contributes most?
• Which vendors need strategic intervention vs monitoring?

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📂 Data Description

The dataset consists of large transactional retail records including:

• Vendor, brand, and item-level identifiers
• Purchase and sales quantities
• Purchase price, actual selling price, freight cost, excise tax
• Inventory movement indicators

Raw data was ingested from CSV files and transformed into analytical tables suitable for aggregation and analysis.

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🧱 Data Pipeline

1. Data Ingestion

• Ingested large transactional CSV datasets into MySQL using Python.
• Handled large file sizes during ingestion to ensure reliable database loading

📓 Notebook: VendorPerformance_ingestion.ipynb

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2. SQL Querying & Feature Engineering

• Executed SQL queries involving joins, filters, and aggregations to create a final analysis-ready dataset.
• Loaded SQL query results into Python DataFrames.
• Derived additional business metrics such as:
  • Profit Margin
  • Freight Ratio
  • Inventory Turnover
  • Vendor Dependency indicators

📓 Notebook: VendorPerformance_SQLAnalysis.ipynb

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3. Exploratory Data Analysis & Business Insights

• Performed exploratory data analysis on vendor and inventory performance.
• Identified:
  • Low-margin and high-risk vendors
  • Low-rotation and unsold inventory
  • Profitability vs sales volume trade-offs
• Visualized trends and patterns to support analytical conclusions.

📓 Notebook: VendorPerformance_PythonAnalysis.ipynb

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📈 Analytical Approach

• Built aggregated vendor and brand performance tables using SQL (CTEs, joins)
• Segmented vendors using percentile-based thresholds on sales and profit margin
• Classified vendors into:
  • High Sales – Low Margin
  • Low Sales – High Margin
  • Low Sales – Low Margin
• Visualized trade-offs using scatter plots with threshold reference lines

Negative profit-margin entries were retained for overall correlation analysis and excluded only during targeted vendor segmentation to avoid distortion.

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📱 Dashboard

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🧰 Tech Stack

• Database: MySQL
• Analysis & Processing: Python (Pandas, Matplotlib, Seaborn)
• Visualization: Power BI
• Tools: Jupyter Notebook

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💡 Key Insights

• A limited number of vendors contributed heavily to revenue, increasing dependency risk.
• Several items showed high inventory value but low sales movement.
• High sales volume did not always correlate with high profitability.

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🎯 Business Impact

This analysis supports:

• Targeted vendor renegotiation strategies
• Inventory liquidation and optimization planning
• Improved vendor prioritization
• Reduction in capital lock-in due to slow-moving stock

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Future Enhancements

• Interactive Power BI dashboard for stakeholder reporting
• Time-based vendor trend analysis
• Automate ingestion and SQL execution using scheduled pipelines