A Comparative Approach to Inventory Optimization

In traditional inventory management, determining reorder points and quantities (R and Q) often relies on textbook formulas that assume a single ordering period. But real-world supply chains, with their dynamic demand patterns and varying lead times, need a more flexible framework. My goal was to create just that: a versatile approach for setting R and Q values in complex situations, including both single and multi-echelon network designs.

Key Takeaways

This project highlights the value of combining simulation and optimization in inventory management. Each method has its place: cut-off thresholds are fast and simple, LP is effective for direct optimization when priorities are clear, and Pareto analysis shines when navigating complex trade-offs. By leveraging these tools, companies can build more responsive and efficient inventory policies that adapt to the realities of continuous ordering and dynamic supply chains.

Sample code here