# Enhancing Supply Chain Resilience: A Machine Learning Approach for Predicting Product Availability Dates Under Disruption

arXiv: https://arxiv.org/abs/2304.14902 · HTML: https://mlsanity.com/paper/2304.14902/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 58 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [An Optimization Framework for Efficient and Sustainable Logistics Operations via Transportation Mode Optimization and Shipment Consolidation: A Case Study for GE Gas Power](https://mlsanity.com/paper/2212.03662/) (2022)
- [Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.04651/) (2022)

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

- [PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data](https://mlsanity.com/paper/2503.12220/) (2025)
- [Censoring-Aware In-Context Learning for Generalized Supplier Lead Time Estimation in Supply Chain Planning](https://mlsanity.com/paper/2607.18530/) (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)

The COVID 19 pandemic and ongoing political and regional conflicts have a highly detrimental impact on the global supply chain, causing significant delays in logistics operations and international shipments. One of the most pressing concerns is the uncertainty surrounding the availability dates of products, which is critical information for companies to generate effective logistics and shipment plans. Therefore, accurately predicting availability dates plays a pivotal role in executing successful logistics operations, ultimately minimizing total transportation and inventory costs. We investigate the prediction of product availability dates for General Electric (GE) Gas Power's inbound shipments for gas and steam turbine service and manufacturing operations, utilizing both numerical and categorical features. We evaluate several regression models, including Simple Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network models. Based on real world data, our experiments demonstrate that the tree based algorithms (i.e., RF and GBM) provide the best generalization error and outperforms all other regression models tested. We anticipate that our prediction models will assist companies in managing supply chain disruptions and reducing supply chain risks on a broader scale.