# Conformal Predictive Distributions for Order Fulfillment Time Forecasting

arXiv: https://arxiv.org/abs/2505.17340 · HTML: https://mlsanity.com/paper/2505.17340/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Computationally efficient versions of conformal predictive distributions](https://mlsanity.com/paper/1911.00941/) (2019)
- [Large-scale probabilistic predictors with and without guarantees of validity](https://mlsanity.com/paper/1511.00213/) (2015)
- [Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems](https://mlsanity.com/paper/2409.07637/) (2024)
- [End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications](https://mlsanity.com/paper/2009.12197/) (2020)
- [Contextual Stochastic Optimization for Omnichannel Multi-Courier Order Fulfillment Under Delivery Time Uncertainty](https://mlsanity.com/paper/2409.06918/) (2024)
- [A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2309.01194/) (2023)
- [Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services](https://mlsanity.com/paper/2505.00375/) (2025)
- [Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry](https://mlsanity.com/paper/2411.17502/) (2024)
- [Tightening optimality gap with confidence through conformal prediction](https://mlsanity.com/paper/2503.04071/) (2025)
- [Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics](https://mlsanity.com/paper/1501.05349/) (2015)
- [SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks](https://mlsanity.com/paper/2504.09680/) (2025)

## Built on by (most cited first, 3 of 3)

- [Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning](https://mlsanity.com/paper/2602.20271/) (2026)
- [Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application](https://mlsanity.com/paper/2606.25362/) (2026)
- [When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization](https://mlsanity.com/paper/2607.18573/) (2026)

## Abstract (arXiv)

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.