forecasting** — identify seasonal variations and product performance.

• 🤝 Enhance supplier management — evaluate supplier reliability and delivery lead times.
• 💰 Maximize profitability — optimize stock levels and reduce holding costs.

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🧠 Technical Stack

Tool	Purpose
SQL	Data querying, cleansing, and aggregation
Power BI	Dashboarding, visualization, KPI tracking
Python	Synthetic data generation, preprocessing, and ETL automation
Excel (optional)	Manual inspection and data formatting

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📊 Core Analyses

Analysis Area	Description
Inventory Valuation & Turnover	Measure how efficiently stock is utilized over time
Product Performance	Identify top-performing and low-performing products
Seasonal Trends	Discover seasonal patterns affecting demand
Supplier Reliability	Evaluate supplier on-time delivery and consistency
Store-Level Performance	Compare metrics like sales, margin, and turnover across stores

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⚙️ Project Structure (Suggested)

Retail-Inventory-Optimization/
├─ SQL/                  # SQL queries and schema scripts
├─ Data/                 # Sample/synthetic datasets
├─ PowerBI/              # Dashboard files (.pbix)
├─ Python/               # Scripts for data generation or preprocessing
├─ Reports/              # Exports or screenshots of dashboards
├─ README.md
└─ LICENSE

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🚀 Quick Start

1️⃣ Clone this repository:

git clone https://github.com/ziaee-mohammad/Retail-Inventory-Optimization.git
cd Retail-Inventory-Optimization

2️⃣ Run SQL scripts to load and analyze data.
3️⃣ Open the Power BI dashboard (RetailDashboard.pbix) to explore metrics and insights.
4️⃣ (Optional) Run Python scripts in /Python to regenerate or preprocess data.

💡 Data used in this project is synthetically generated for demonstration purposes.

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📈 Example Insights

Insight	Observation
Overstock vs Stockout Ratio	Balanced inventory achieved with optimized reorder levels
Top Performing Products	Electronics and Home Essentials drive highest turnover
Supplier Efficiency	85% of suppliers deliver within SLA window
Seasonal Demand Peaks	Notable spikes during Q4 and festive seasons

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📜 License

This project is released under the MIT License — you may use, modify, and share it with attribution.

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👨🏻‍💻 Author

Mohammad Ziaee
📍 Computer Science Graduate Student | AI & Data Science Enthusiast
📧 moha2012zia@gmail.com
🔗 GitHub Profile 👉 Instagram: @ziaee_mohammad

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🏷 Tags

data-science
business-intelligence
analytics
dashboard
sql
power-bi
python
data-analysis
reporting
inventory-optimization
retail