Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

The Application of Machine Learning in Improving Inventory Management in U.S. Mobile Device Manufacturing

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  • Dr. In-Soo Jung
Dr. In-Soo Jung
Professor of Automotive Engineering, Dong-A University, South Korea
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Published 09-09-2024

Keywords

  • Inventory Management,
  • Mobile Device Manufacturing
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. In-Soo Jung, “The Application of Machine Learning in Improving Inventory Management in U.S. Mobile Device Manufacturing”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 177–188, Sep. 2024, Accessed: Nov. 12, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/156

Abstract

Inventory management in manufacturing industries is a critical aspect that significantly impacts operational efficiency. Effective inventory management aims to maximize service levels while minimizing holding costs, as an unbalanced inventory system can lead to production stoppages, back-ordered demands, lost sales, and additional expenses [1]. The historical context of inventory management evolution highlights the shift towards data-driven approaches, particularly the application of machine learning algorithms to solve inventory-related challenges in data-rich environments [2]. This section sets the stage for the subsequent discussions by providing an overview of the key principles and practices involved in managing inventory within manufacturing settings, emphasizing the importance of leveraging new technologies to improve efficiency in supply chains.

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