Summary of Enhancing Supply Chain Security with Automated Machine Learning, by Haibo Wang et al.
Enhancing supply chain security with automated machine learning
by Haibo Wang, Lutfu S.Sua, Bahram Alidaee
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Economics (econ.GN); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an automated machine learning (ML) framework for enhancing supply chain security. The increasing complexity of global supply chains has led to new challenges, such as supply chain disruptions, material shortages, and inflation. To address these issues, the authors apply ML methods like Random Forest, XGBoost, LightGBM, and Neural Networks to detect fraudulent activities, predict maintenance needs, and forecast material backorders. The framework is tested on datasets of varying sizes, achieving high accuracy rates for fraud detection (88%), machine failure prediction (93.4%), and material backorder prediction (89.3%). Hyperparameter tuning further improves the performance of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to make supply chains safer and more efficient. Right now, there are lots of problems with global supply chains, like things getting stuck at ports or not having enough materials. The authors use special computer programs called machine learning algorithms to solve these issues. They test their ideas on big datasets and get really good results – 88% accurate for detecting fake activities, 93.4% for predicting when machines will break down, and 89.3% for forecasting when we’ll run out of materials. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Random forest » Xgboost
