🤖 AI Summary
Order fulfillment time prediction in e-commerce logistics faces challenges in modeling uncertainty and insufficient coverage by traditional rule-based approaches. To address these, this paper proposes the first distributional forecasting framework that jointly ensures statistical validity and business sensitivity. It introduces Conformal Predictive Systems and the Cross Venn-Abers Predictor—novel to fulfillment time prediction—to deliver rigorous coverage probability guarantees. We further design cost-sensitive point prediction rules to prioritize late-delivery detection. The method integrates spatiotemporal fine-grained feature engineering with tree-based and neural network models, and employs Venn-Abers calibration for principled uncertainty quantification. Evaluated on large-scale industrial data, our approach achieves theoretical validity in distributional forecasts, improves point prediction accuracy by 14%, and boosts late-delivery identification rate by 75%, significantly outperforming existing rule-based systems.
📝 Abstract
Accurate estimation of order fulfillment time is critical for e-commerce logistics, yet traditional rule-based approaches often fail to capture the inherent uncertainties in delivery operations. This paper introduces a novel framework for distributional forecasting of order fulfillment time, leveraging Conformal Predictive Systems and Cross Venn-Abers Predictors--model-agnostic techniques that provide rigorous coverage or validity guarantees. The proposed machine learning methods integrate granular spatiotemporal features, capturing fulfillment location and carrier performance dynamics to enhance predictive accuracy. Additionally, a cost-sensitive decision rule is developed to convert probabilistic forecasts into reliable point predictions. Experimental evaluation on a large-scale industrial dataset demonstrates that the proposed methods generate competitive distributional forecasts, while machine learning-based point predictions significantly outperform the existing rule-based system--achieving up to 14% higher prediction accuracy and up to 75% improvement in identifying late deliveries.