Zero-Inflated Neural Demand Model (ZINDM) for Short-Horizon Sparse Demand Forecasting and Behaviour Analysis
Abstract
Short-horizon demand forecasting for automotive spare parts remains a challenging task due to sparse observations, frequent zero-demand periods, and limited historical data at the customer-product level. While neural forecasting models have shown promising predictive capabilities, their practical adoption in operational environments is often constrained by limited transparency and trust. In such contexts, understanding model behavior is as critical as achieving high forecast accuracy. This study presents an explainable neural forecasting framework designed to support short-horizon demand prediction under data scarcity. The approach combines zero-inflated modelling, hierarchical representations, and attention-based temporal mechanisms to capture recent demand dynamics while enabling controlled information sharing across related series. Rather than focusing exclusively on numerical performance, the framework emphasizes interpretability through post-hoc feature attribution and temporal relevance analysis. The proposed model is evaluated on a real-world automotive spare parts dataset using benchmark forecasting methods as references. Model behavior is analyzed through explainability tools and illustrative diagnostics, including temporal attention patterns and residual analyses, to assess transparency and stability. The results demonstrate that the framework effectively captures short-term demand signals and provides interpretable insights into the factors influencing predictions. These findings highlight the potential of explainable neural forecasting models as decision-support tools for spare parts management in data-scarce industrial settings.