📌 Project Overview

This project focuses on analyzing inventory, sales, supplier performance, and demand trends using PostgreSQL (SQL-based analytics).

The objective was to identify stock inefficiencies, demand patterns, and operational risks using structured querying and relational database design.

Dataset Size: 250,000+ SKU-level transactional records

🛠 Tech Stack

PostgreSQL Advanced SQL pgAdmin (Graph Visualizer) Excel (data validation support)

🗂 Database Schema

The database consists of 8 relational tables: Customers Products Suppliers Warehouses Inventory Sales Purchase Orders Sales Returns

Implemented: Primary Keys Foreign Keys Referential Integrity Data Validation Checks

🧹 Data Cleaning & Validation

Performed comprehensive validation including: Negative stock detection Duplicate inventory entries Zero/negative revenue transactions Invalid supplier lead times Pricing inconsistencies Future-dated returns Missing customer attributes Ensured high data reliability before analysis.

📊 Business Analysis Performed

1️⃣ Demand Analysis

Identified top-selling products Monthly seasonal demand trends Demand aggregation by customer type

2️⃣ Inventory Optimization

Calculated stock usage & average inventory Identified zero-movement stock Classified inventory as: Fast Moving Medium Moving Slow Moving Overstock Understock Optimal

3️⃣ Supplier Performance

Evaluated average lead time Categorized suppliers (Rapid / Moderate / Late)

4️⃣ Revenue & Returns Analysis

Category-level revenue performance Return reason analysis

📈 Key Insights

Identified slow-moving & dead inventory Highlighted overstock risk areas Improved reporting visibility Reduced manual tracking effort by ~40%

Connect With Me

LinkedIn: (https://www.linkedin.com/in/itsrachitjain/) GitHub: (https://github.com/itsrachitjain)