Supply chain analytics and Six Sigma project analyzing warehouse efficiency, processing time, and operational errors using SQL, Python, and Tableau.
This project analyzes warehouse order processing performance to identify operational inefficiencies and improvement opportunities.
The analysis follows Six Sigma DMAIC methodology and uses modern data analytics tools.
Technologies used:
- SQL → Operational data analysis
- Python → Statistical modeling and simulation
- Tableau → Executive dashboard visualization
The dataset simulates warehouse order processing operations including:
- Order ID
- Warehouse zone
- Picker ID
- Items picked
- Processing time
- Errors
- Shift
- Day of week
Dataset file:
tesla_warehouse_orders_5000.csv
SQL queries were used to analyze warehouse performance metrics including:
- Average order processing time
- Error rates by warehouse zone
- Orders processed by day
- Picker performance analysis
SQL script:
tesla_warehouse_analysis.sql
Python was used to perform deeper statistical analysis and modeling.
tesla_warehouse_analysis.py
Identifies the warehouse zones responsible for the majority of operational errors using the 80/20 principle.
pareto_analysis.py
Builds a statistical model to predict processing time based on operational variables such as:
- Items picked
- Shift
- Warehouse zone
regression_model.py
Simulates thousands of warehouse processing scenarios to estimate future performance variability and risk.
monte_carlo_simulation.py
The interactive dashboard visualizes operational insights including:
- Orders processed by day
- Error rates by warehouse zone
- Processing time trends
- Picker efficiency comparison
This dashboard allows decision-makers to quickly identify bottlenecks and performance gaps.
This project demonstrates real Six Sigma Black Belt analytics capabilities:
- DMAIC methodology
- SQL operational analysis
- Python statistical modeling
- Pareto root cause analysis
- Control charts
- Process improvement planning
- Executive dashboard reporting
tesla-warehouse-operations-dashboard
│
├── tesla_warehouse_orders_5000.csv
├── tesla_warehouse_analysis.sql
├── tesla_warehouse_analysis.py
├── pareto_analysis.py
├── regression_model.py
├── monte_carlo_simulation.py
├── tesla_dashboard.png
└── README.md
- SQL
- Python
- Pandas
- NumPy
- Scikit-learn
- Tableau
- Data Visualization
- Supply Chain Analytics
- Six Sigma
Alfred Quenum Data Analytics & Operations Optimization Portfolio Project