🏭 Supply Chain SQL EDA

📌 Project Overview

This project demonstrates Exploratory Data Analysis (EDA) on a Supply Chain Database using Microsoft SQL Server (T-SQL). The goal is to transform raw operational data into actionable business insights to improve supply chain visibility, efficiency, and profitability.

The project follows a multi-layered data architecture:

• Bronze Layer: Raw staging data (Bronze.Stg_SupplyChainData)
• Silver Layer: Cleaned and transformed data (Silver.Transform_SupplyChainData)
• Gold Layer: Aggregated analytical views (Gold.VW_*) for reporting

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🧱 Methodology

1. Data Exploration

   • Count rows and examine schema for Bronze & Silver layers.
   • Review column details, nullability, and data types.

2. Dimensions Exploration

   • Analyze core dimensions: Products, Customers, Stores, Shipping, Orders.
   • Identify unique categories, statuses, and customer locations.

3. Date Range Exploration

   • Determine order and shipping timelines.
   • Calculate shipping delays and operational duration.

4. Measures Exploration (Key Metrics)

   • Total orders, revenue, profit, and average sales.
   • Unique products sold, total product quantity, and customer counts.

5. Magnitude Analysis

   • Highest discounts, top-selling products, and country-wise sales distribution.
   • Category-wise revenue, average price, and customer revenue contributions.

6. Ranking Analysis

   • Top 5 revenue-generating products & top 5 by quantity sold.
   • Bottom 5 underperforming products.
   • Top 10 customers by revenue and 3 customers with the fewest orders.

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💡 Key Insights

• Top 5 products generate the majority of revenue.
• Shipping delays impact regional performance.
• Category-wise profitability highlights high-value areas.
• Customer lifetime value patterns reveal retention opportunities.

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🛠️ Tools & Technologies

• Microsoft SQL Server (T-SQL)
• CTEs, Window Functions, Aggregations for EDA
• Views & Stored Procedures for reporting
• Optional: Power BI / Excel for visualizations