Safety Stock Model Selection Optimization for Budget-Constrained Multi-Item Inventory Management: A Scalable Framework

Effective safety stock management is essential for balancing service levels and inventory holding costs in supply chains. Conventional analytical models often rely on unrealistic assumptions, such as normally distributed demand, while simulation-based methods, though more accurate and flexible, are computationally impractical for firms managing thousands of items. This paper proposes the Safety Stock Model Selection Optimization (SSMSO) framework, where a model refers to a specific approach or algorithm used to determine safety stock levels for a single item and is evaluated by the win rate metric—defined as the proportion of periods with no stockouts. SSMSO determines safety stock levels by optimally selecting a model for each item from a list of individual models, thereby reducing reliance on specific probabilistic assumptions. The optimization problem is formulated as a binary integer linear program (ILP) that maximizes the total win rate while ensuring the total safety stock cost remains within budget. By leveraging LP solvers with special ordered set constraints, SSMSO efficiently handles thousands of items, scaling significantly better than nonlinear programming-based methods, which struggle with convergence for large datasets. Experimental results on a real-world dataset demonstrate the effectiveness of SSMSO in reducing shortage rates while maintaining budget feasibility, making it a practical and scalable solution for multi-item safety stock optimization.