# Enhancing supply chain security with automated machine learning

arXiv: https://arxiv.org/abs/2406.13166 · HTML: https://mlsanity.com/paper/2406.13166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Importance of Tuning Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/2007.07588/) (2020)
- [Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems](https://mlsanity.com/paper/1908.05287/) (2019)

## Built on by (most cited first, 1 of 1)

- [Scalable GPU-Based Integrity Verification for Large Machine Learning Models](https://mlsanity.com/paper/2510.23938/) (2025)

## Abstract (arXiv)

The increasing scale and complexity of global supply chains have led to new challenges spanning various fields, such as supply chain disruptions due to long waiting lines at the ports, material shortages, and inflation. Coupled with the size of supply chains and the availability of vast amounts of data, efforts towards tackling such challenges have led to an increasing interest in applying machine learning methods in many aspects of supply chains. Unlike other solutions, ML techniques, including Random Forest, XGBoost, LightGBM, and Neural Networks, make predictions and approximate optimal solutions faster. This paper presents an automated ML framework to enhance supply chain security by detecting fraudulent activities, predicting maintenance needs, and forecasting material backorders. Using datasets of varying sizes, results show that fraud detection achieves an 88% accuracy rate using sampling methods, machine failure prediction reaches 93.4% accuracy, and material backorder prediction achieves 89.3% accuracy. Hyperparameter tuning significantly improved the performance of these models, with certain supervised techniques like XGBoost and LightGBM reaching up to 100% precision. This research contributes to supply chain security by streamlining data preprocessing, feature selection, model optimization, and inference deployment, addressing critical challenges and boosting operational efficiency.