Inventory Optimization Project (Python)

Overview

This project analyzes daily demand data for multiple SKUs and develops basic inventory policies using EOQ, safety stock, and reorder point models.

The goal is to simulate a realistic distribution environment and support data-driven inventory decisions.

Key Steps

1. Data cleaning and preprocessing
2. Demand aggregation (daily to monthly)
3. Demand variability analysis
4. EOQ calculation
5. Safety stock and reorder point estimation
6. Sensitivity analysis for different service levels

Tools

• Python
• pandas
• numpy
• matplotlib

Key Insights

• High-variability SKUs require significantly higher safety stock at higher service levels.
• High-demand SKUs show larger reorder points and more frequent ordering cycles.
• Service level decisions strongly affect inventory holding costs.

Files

• inventory_optimization.py → main analysis script
• data/ → input dataset
• outputs/ → results and charts