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arXiv · 2505.17340

Conformal Predictive Distributions for Order Fulfillment Time Forecasting

Tinghan Ye·Amira Hijazi·Pascal Van Hentenryck
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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.

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Tinghan Ye, Amira Hijazi, Pascal Van Hentenryck. 2025-08-01. Conformal Predictive Distributions for Order Fulfillment Time Forecasting. https://arxiv.org/abs/2505.17340

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