Multi-Objective Optimization Framework for Reliable Safety Stock Decisions With Intermittent Demand Forecasting

Abstract

Safety stock (SS), encompassing reorder point (RP) and maximum stock (MS), is designed to minimize inventory cost while maintaining maintenance safety for manufacturing enterprises. In recent years, advanced computational techniques have been applied to forecast spare parts demand (SPD), enabling dynamic updates to SS settings. However, existing methods have often failed to produce reliable SS decisions when confronted with significant demand volatility and random failures. The reliability of SS decisions remains an open problem and is still in its early stages of investigation. To address this issue, in this paper, a reliable SS model with uncertainty evaluation of SPD is proposed in this paper. Initially, a basic setting of RP and MS is derived on a three-level warehousing architecture by simultaneously minimizing excess inventory cost and shortage cost. Building upon this foundation, a novel SS reliability metric is introduced by integrating static information (inventory coverage and emergency replenishment rate) with dynamic information (decision compactness ratio). To enhance the decision compactness ratio, a multivariate intermittent time series forecasting method based on an improved graph neural network is developed. Accurate forecasting with a confidence interval is achieved through bootstrap resampling. Finally, a reliable RP and MS configuration is obtained by incorporating the forecasting interval into the basic stock setting. Validation is conducted using an actual spare parts dataset from a large rail transit manufacturing enterprise in China. The experimental results demonstrate that the proposed model not only achieves higher SPD prediction accuracy but also improves inventory turnover and coverage while significantly enhancing reliability.