Process-Informed Deep Learning for Enhanced Order Fulfillment Cycle Time Prediction in On-Demand Grocery Retailing

Accurate prediction of Order Fulfillment Cycle Time (OFCT) is essential for improving customer satisfaction and operational efficiency within the domain of on-demand grocery retailing (OGR). OGR platforms typically rely on Front Distribution Centers (FDCs) to manage inventory and deploy dedicated fleets for last-mile delivery to fulfill customer demands. Orders are processed at FDCs initially and then dispatched to delivery fleets. OFCT is influenced by a multitude of factors such as order volume, processing capabilities, delivery capacities, and dispatching strategies. These factors pose significant challenges to refining OFCT prediction accuracy. This paper presents an innovative deep learning model informed by a detailed comprehension of the order fulfillment process, with the objective of significantly enhancing OFCT prediction precision. We employ Recurrent Neural Network (RNN) blocks to dynamically evaluate the workload across processing and delivery stages. To address the interactions among orders and the impact of latent courier dynamics on order prioritization, we incorporate a suite of specialized attention modules into our framework. Our approach further employs Deep Bayesian Multi-Target Learning (DBMTL) to discern the sequential interactions between various stages of order fulfillment, thereby elucidating the influence of earlier stages on subsequent ones. Through online experiments on Meituan-Maicai, one of the biggest OGR platforms in China, our model demonstrates its superiority by outperforming well-acknowledged and advanced baselines. Furthermore, we assess the contributions of specific designs in our model through ablation studies. Our research presents a notable advancement in OFCT prediction, providing valuable insights for OGR platforms seeking to optimize their fulfillment operations and enhance customer experiences.