A hybrid neural framework for short, sparse, and zero-dominated demand forecasting: Automotive spare parts application

Short-horizon demand forecasting in industrial settings is often characterized by sparse observations, frequent zero-demand periods, and limited historical data at the customer – product level. These conditions pose significant challenges for conventional forecasting models, which treat demand as a continuous process and do not explicitly account for zero inflation. This study proposes a zero-inflated neural forecasting framework that decomposes demand into occurrence and magnitude components while simultaneously leveraging shared information across related time series through embedding-based representations and lightweight temporal modeling. The framework is evaluated using an industrial automotive spare parts dataset characterized by high sparsity and short observation windows. Results show that the proposed approach achieves competitive forecasting performance compared to strong benchmark models, including LightGBM with a Poisson objective. Importantly, the findings reveal that explicitly modeling demand occurrence substantially improves accuracy in zero-demand periods, while alternative models tend to perform better in magnitude estimation during non-zero demand. These results highlight a fundamental trade-off in sparse demand forecasting between occurrence detection and magnitude estimation, suggesting that model selection should align with operational priorities. The proposed framework provides a practical and interpretable approach for short-horizon decision support in intermittent demand environments.