📦 Supply Chain Performance Analytics

🎯 Project Overview

End-to-end data analytics project analyzing supply chain performance of a global electronics company retailer across 6 years (2016-2021).

🛠️ Tools Used

Tool	Purpose
Excel / Power Query	Data cleaning
SQL Server (SSMS)	Data modeling & analysis
Python 3.10	EDA, forecasting, optimization
Power BI	Interactive dashboard

📊 Dataset

• Source 1: Maven Analytics — Global Electronics Retailer
• Source 2: Figshare Brunel University — Supply Chain Logistics
• Size: 62,884 sales transactions + 9,215 freight orders
• Period: 2016 — 2021

🔑 Key Findings

• 💰 Total Revenue: $55.76M
• 📦 On-Time Delivery Rate: 47.28% (Critical Issue)
• 🏆 Top Category: Computers ($19.3M — 34.6%)
• 🌎 Top Region: North America
• 📅 Peak Month: February (Valentine's Day)
• 🚚 Best Carrier: V444_0 (Score: 70/100)
• 🛒 Online vs In-Store: 20.4% vs 79.6%

📁 Project Structure

supply_chain_analytics/ ├── 01_excel/ — Raw and cleaned datasets and Power Query workbooks ├── 02_sql/ — Schema and queries ├── 03_python/ — Analysis scripts ├── 04_outputs/ — Charts and CSV exports └── 05_powerbi/ — Dashboard file

🗄️ SQL Analysis

• 8 analytical queries
• 3 advanced queries (CTEs, Window Functions)
• Key metrics: Revenue, Delivery Rate, Carrier Performance

🐍 Python Analysis

• EDA with 15 visualizations
• Carrier performance scorecard
• 6-month demand forecast (Facebook Prophet)
• Inventory optimization (EOQ model)

📊 Power BI Dashboard

5 interactive pages:

1. Executive Summary
2. Sales Performance
3. Delivery & Carrier Analysis
4. Demand Forecast
5. Inventory Optimizer

💡 Business Recommendations

1. Delivery — Only 47% on-time rate needs urgent fix
2. Carrier — Review V44_3 contract (lowest score)
3. Inventory — 7% critical risk products need attention
4. Online — Grow online channel (currently only 20%)
5. Seasonality — Increase stock before February peak

🚀 How to Run

1. Clone the repository
2. Install Python libraries: pip install -r requirements.txt.
3. Update SQL connection in db_connection.py.
4. Run Python scripts in order (01 to 04)
5. Open the Power BI file and refresh data