A Machine Learning-powered inventory optimization system that forecasts product demand, calculates safety stock levels, determines reorder points, and helps businesses reduce stockouts while improving inventory efficiency.
This project combines Demand Forecasting and Inventory Optimization techniques to support data-driven supply chain decisions.
- Forecast future product demand using historical sales data
- Optimize inventory planning
- Calculate Safety Stock
- Determine Reorder Points
- Analyze forecasting accuracy
- Visualize insights through an interactive dashboard
Organizations frequently face:
- Overstocking and excess inventory costs
- Stockouts and lost sales opportunities
- Inaccurate demand planning
- Poor inventory turnover
- Inefficient replenishment strategies
This solution helps businesses improve inventory management through predictive analytics and machine learning.
- Python
- Pandas
- NumPy
- Scikit-Learn
- XGBoost
- Matplotlib
- Seaborn
- Plotly
- Streamlit
- MLflow
Demand-Forecasting-Inventory-Optimization
│
├── dashboards/
├── models/
├── notebooks/
├── reports/
├── screenshot/
├── src/
├── requirements.txt
├── run_project.py
└── README.md
- Historical sales analysis
- Future demand prediction
- Forecast performance evaluation
- Trend analysis
- Safety Stock Calculation
- Reorder Point Calculation
- Inventory Risk Analysis
- Stock Planning Support
- MLflow Tracking
- Logging
- Health Checks
- Load historical sales data
- Perform data preprocessing
- Engineer forecasting features
- Train XGBoost model
- Generate demand forecasts
- Evaluate model performance
- Calculate inventory metrics
- Visualize results through Streamlit dashboard
pip install -r requirements.txtstreamlit run dashboards/app.pypython run_project.py- Real-time demand forecasting
- Multi-product inventory planning
- Automated replenishment recommendations
- Cloud deployment
- Advanced forecasting models (LSTM, Prophet)
Shubham Panchal
Data Analytics | Data Science | Machine Learning | Business Intelligence