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Enhancing supply chain security with automated machine learning

Author

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  • Haibo Wang
  • Lutfu S. Sua
  • Bahram Alidaee

Abstract

This study tackles the complexities of global supply chains, which are increasingly vulnerable to disruptions caused by port congestion, material shortages, and inflation. To address these challenges, we explore the application of machine learning methods, which excel in predicting and optimizing solutions based on large datasets. Our focus is on enhancing supply chain security through fraud detection, maintenance prediction, and material backorder forecasting. We introduce an automated machine learning framework that streamlines data analysis, model construction, and hyperparameter optimization for these tasks. By automating these processes, our framework improves the efficiency and effectiveness of supply chain security measures. Our research identifies key factors that influence machine learning performance, including sampling methods, categorical encoding, feature selection, and hyperparameter optimization. We demonstrate the importance of considering these factors when applying machine learning to supply chain challenges. Traditional mathematical programming models often struggle to cope with the complexity of large-scale supply chain problems. Our study shows that machine learning methods can provide a viable alternative, particularly when dealing with extensive datasets and complex patterns. The automated machine learning framework presented in this study offers a novel approach to supply chain security, contributing to the existing body of knowledge in the field. Its comprehensive automation of machine learning processes makes it a valuable contribution to the domain of supply chain management.

Suggested Citation

  • Haibo Wang & Lutfu S. Sua & Bahram Alidaee, 2024. "Enhancing supply chain security with automated machine learning," Papers 2406.13166, arXiv.org.
  • Handle: RePEc:arx:papers:2406.13166
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    References listed on IDEAS

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    1. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    2. Michaela Fox & Mike Mitchell & Moira Dean & Christopher Elliott & Katrina Campbell, 2018. "The seafood supply chain from a fraudulent perspective," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(4), pages 939-963, August.
    3. Ying Liu & Lihua Huang, 2020. "Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477209, January.
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