Enhancing supply chain security with automated machine learning

TL;DR

This paper introduces an automated machine learning framework to improve supply chain security by detecting fraud, predicting maintenance, and forecasting backorders, achieving high accuracy and operational efficiency.

Contribution

It presents a novel automated ML approach tailored for supply chain security, integrating data preprocessing, feature selection, and model optimization.

Findings

Fraud detection accuracy of 88% with sampling methods

Machine failure prediction accuracy of 93.4%

Material backorder prediction accuracy of 89.3%

Abstract

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.