Conformal Predictive Distributions for Order Fulfillment Time Forecasting
This addresses the critical need for accurate and reliable fulfillment time predictions in e-commerce logistics, representing a domain-specific advancement with competitive improvements.
The paper tackles the problem of forecasting order fulfillment times in e-commerce logistics by developing a novel framework using Conformal Predictive Systems and Cross Venn-Abers Predictors to provide distributional forecasts with coverage guarantees, achieving up to 14% higher prediction accuracy and up to 75% improvement in identifying late deliveries compared to existing rule-based systems.
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.