A simulation-based hybrid causal predictive framework for stockout risk analysis in supply chain

Abstract

Stockout risk is a persistent challenge in supply chain management, undermining both operational efficiency and customer satisfaction. This study adopts a multi-method approach to investigate the causal effect of lead time on stockout risk by integrating causal inference techniques with predictive analytics. The proposed framework combines Propensity Score Matching (PSM), Instrumental Variables (IV-2SLS), Inverse Probability Weighting (IPW), and Doubly Robust Estimation (DRE) alongside machine learning (ML) algorithms and time series forecasting. Using a dataset of 20,000 supply chain incidents, the study estimates the Average Treatment Effect (ATE) and evaluates predictive model performance. PSM generated the most credible ATE (0.882), confirming a strong causal link between lead time and stockout risk. IV analysis using supplier distance as an instrument yielded a reduced and statistically insignificant ATE (0.5535, p = 0.3148), suggesting instrument weakness. Among ML models, Random Forest and LightGBM achieved superior predictive accuracy (R 2  = 0.25; MSE = 0.736), while Moving Average forecasting effectively captured stockout patterns over time (R 2 = 0.883). The findings identify PSM as the most robust technique for causal inference. This study advances the literature by integrating causal inference, ML, and time series methods, offering practical, data-driven insights to strengthen operational resilience and guide proactive inventory management.