Supply Chain Analytics Dashboard

An end-to-end supply chain analytics project tracking inventory health, supplier performance, and shipment delays across warehouses and suppliers.

Dashboard Preview

Tech Stack

• Python — Data generation & analysis (Pandas, Faker, SQLAlchemy)
• PostgreSQL — Relational database with 6 tables
• Power BI — Interactive 5-page dashboard
• Excel — Exported analytical reports

Project Structure

supply-chain-analytics/ ├── data/ # Generated CSV and Excel files ├── sql/ │ ├── 01_create_tables.sql # Database schema │ └── 02_load_data.sql # Data loading script ├── python/ │ ├── load_to_postgres.py # Load CSVs to PostgreSQL │ └── analysis.py # Business metrics analysis ├── generate_data.py # Synthetic data generation └── Supply_Chain_Dashboard.pbix # Power BI dashboard

Dataset Overview

Table	Rows	Description
Warehouses	10	Locations, capacity, utilization
Suppliers	50	Country, reliability score, defect rate
Products	300	6 categories, cost, reorder levels
Inventory	300	Stock status, days of supply
Purchase Orders	2,000	Procurement transactions
Shipments	1,500+	Delivery tracking, delays, carriers

Key Business Insights

• Only 28.8% of shipments delivered on time — major logistics gap identified
• Average shipment delay of 1.9 days across all carriers
• Multiple products flagged for critical reorder urgency
• Warehouse utilization imbalances detected across 10 locations

Dashboard Pages

1. Executive Summary — KPI cards, inventory health, carrier performance
2. Inventory Analysis — Stock status, reorder urgency, days of supply
3. Supplier Performance — On-time rates, defect rates, scorecards
4. Order & Delivery Tracking — Monthly trends, delay distribution
5. Warehouse Operations — Capacity vs usage, utilization levels

How to Run

1. Install dependencies:

   pip install pandas faker sqlalchemy psycopg2-binary openpyxl

2. Generate data:

   python generate_data.py

3. Create PostgreSQL database supply_chain_db and run sql/01_create_tables.sql
4. Load data:

   python python/load_to_postgres.py

5. Run analysis:

   python python/analysis.py

6. Open Supply_Chain_Dashboard.pbix in Power BI Desktop