Explainable Retail Demand Forecasting: A Hybrid LightGBM-SHAP Framework for Inventory Optimization

Gradient boosting models predict retail demand effectively, yet inventory planners distrust predictions they cannot interpret. This paper addresses the trust gap through a four-level Explainable AI (XAI) framework that pairs LightGBM prediction with layered interpretability: Permutation Feature Importance (PFI) for global ranking, Partial Dependence Plots (PDP) for marginal effects, SHapley Additive exPlanations (SHAP) for instance-level attribution, and Individual Conditional Expectation (ICE) plots for heterogeneity detection. ICE analysis, the key extension beyond prior work, reveals when averaged trends mask divergent product behavior, justifying segment-specific inventory policies.Validated on the UCI Online Retail II dataset with 41 engineered features, the proposed model achieves RMSE of 33.71 and R2of 0.243 on inherently intermittent stock-keeping unit (SKU)-level daily demand, significantly outperforming most baselines (paired t-test, p2= -0.064 upon removal). ICE heterogeneity analysis identifies product clusters where temporal features produce opposite effects, demonstrating that uniform reorder policies systematically misallocate inventory for specific segments.