order-fulfillment

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 achieves up to 14% higher prediction accuracy and up to 75% improvement in identifying late deliveriesc compared to rule-based methods.

Online retailers typically decide which warehouse and carrier should fulfill each order using simple cost-minimization rules that ignore how orders could be batched together or how reliable a given carrier actually is. This paper develops a Contextual Stochastic Optimization framework that folds distributional forecasts of delivery delays directly into the fulfillment decision, tested on a real dataset with tens of thousands of products and up to thousands of fulfillment options. The framework is the first to combine multi-courier, omnichannel fulfillment with delivery-time uncertainty, and it substantially improves on-time delivery accuracy while letting retailers trade off cost against delivery risk.