Decoupling Safety Stock and Replenishment Decisions: A Data-Driven Hybrid Risk-Value Framework for Resilient Industrial Inventories

Abstract

Background: Traditional value-based ABC inventory classification allocates protection according to economic value, overlooking operational risk factors such as demand variability, lead time, and assembly criticality, and it couples safety-stock and replenishment-cycle decisions. Methods: We propose a Hybrid Risk-Value framework that decouples these two decisions: stock-keeping units (SKUs) are segmented by multivariate K-means clustering on operational risk variables to set safety-stock factors (Z), while ABC economic value sets replenishment cycle coverages (d). The framework is validated through stochastic discrete-event simulation on an anonymized dataset of 200 SKUs from an automotive supplier, under base, high-demand-variability, and extended-lead-time scenarios (20 replications each), and is benchmarked against both classic ABC and a coupled ABC-XYZ policy. Results: Across all scenarios, the Hybrid framework reduces average inventory investment and total logistical cost by approximately 26–28% relative to ABC (p<0.001) while maintaining the service level; stockout days remain statistically unchanged except under extended lead times. The coupled ABC-XYZ benchmark performs almost identically to ABC, indicating that the gains arise from decoupling rather than from variability-based segmentation alone. Conclusions: Decoupling safety-stock and replenishment decisions offers a capital-efficient, data-driven alternative to static financial segmentation for resilient industrial inventories.