Conformal Predictive Distributions for Order Fulfillment Time Forecasting
Tinghan Ye, Amira Hijazi, Pascal Van Hentenryck

TL;DR
This paper presents a new distributional forecasting framework for order fulfillment time in e-commerce, using conformal predictive systems and machine learning to improve accuracy and reliability over traditional rule-based methods.
Contribution
It introduces a novel, model-agnostic framework combining conformal prediction and spatiotemporal features for more accurate and reliable order fulfillment time forecasts.
Findings
Distributional forecasts are competitive and reliable.
Machine learning point predictions outperform rule-based systems.
Achieved up to 14% higher accuracy and 75% better late delivery detection.
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,…
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