Improving high-frequency demand forecasts for omnichannel grocery retail

The rise of omnichannel grocery retail has introduced significant operational complexities, emphasizing the importance of high-frequency demand forecasting. In this context, achieving both accuracy and computational efficiency with standard forecasting tools remains a challenge. Addressing these limitations, we propose a novel framework that integrates deep learning models with a decoupled approach that separates structural demand modeling from short-term fluctuation prediction to enhance prediction accuracy. Our method combines neural hierarchical interpolation for time series forecasting (N-HiTS) and a mixture density network (MDN) to capture short-term fluctuations and structural demand patterns, respectively. This framework is extended to probabilistic forecasting, comparing quantile-based and distributional models, both with and without the decoupling approach. Empirical validation using data from a leading on-demand delivery service demonstrates significant improvements in deep learning methods over traditional ARIMA methods and the industry-standard gradient boosting machine (GBM), and validates the effectiveness of the decoupling approach. The new framework reduces the mean absolute percentage error (MAPE) for point estimates from 23.00% to 14.31% (a 37.78% reduction) and the continuous ranked probability score (CRPS) from 10.85 to 2.34 (a 78.44% reduction). These findings can provide grocery e-commerce companies with valuable insights for optimizing inventory management, driver scheduling, and overall operational efficiency.