This project focuses on developing predictive models for demand forecasting and optimizing inventory management strategies using advanced analytics techniques.
- File:
Demand_Foreasting_Inventory_Optimization.ipynb - Content: Comprehensive business data including:
- Historical demand data
- Inventory levels
- Supply chain metrics
- Lead times
- Seasonal patterns
-
Demand Forecasting
- Time series analysis
- Seasonal pattern identification
- Trend analysis
- Forecast accuracy metrics
-
Inventory Optimization
- Safety stock calculation
- Reorder point determination
- Order quantity optimization
- Stock level analysis
-
Supply Chain Analytics
- Lead time analysis
- Service level optimization
- Cost optimization
- Risk assessment
- Python
- Pandas for data processing
- Statsmodels for time series analysis
- Scikit-learn for machine learning
- Plotly for interactive visualizations
- Demand patterns and trends
- Optimal inventory levels
- Cost reduction opportunities
- Service level improvements
The project implements advanced forecasting models and inventory optimization techniques through interactive Jupyter notebooks with detailed documentation and visualizations.