A Python-based inventory optimization tool that uses Linear Programming to minimize annual inventory costs for grocery retail operations.
Developed an optimization model for grocery store inventory management to determine optimal safety stock levels, reorder quantities, and ordering frequencies for dairy products and pantry staples.
- Linear Programming optimization using PuLP library
- Dual-mode analysis: predefined products and custom parameter inputs
- Comprehensive cost analysis with before/after optimization comparison
- Multi-constraint optimization incorporating:
- Demand variability
- Vendor lead times
- Product shelf-life constraints
- Service level requirements (95%)
- Language: Python
- Libraries: PuLP, NumPy, Pandas, Math
- Algorithm: Linear Programming with binary variables for order quantity selection
- Constraints: Safety stock minimums, shelf-life limits, service level optimization
- Achieved up to 49.7% reduction in annual inventory costs
- Optimized inventory policies across multiple product categories
- Generated actionable recommendations for safety stock and reorder frequencies
- Demonstrated significant cost savings through data-driven optimization
This tool enables grocery store managers to:
- Make data-driven inventory decisions
- Reduce carrying costs while maintaining service levels
- Prevent stockouts and overstocking
- Optimize cash flow through better inventory management
- Install required libraries:
pip install pulp pandas numpy - Run the Jupyter notebook
- Choose between:
- Predefined product analysis (uses built-in product parameters)
- Custom input mode (enter your own product data)