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

The Application of Machine Learning in Enhancing Product Customization in American Manufacturing: Techniques and Real-World Examples

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  • Dr. Aïcha Belhajjami
Dr. Aïcha Belhajjami
Associate Professor of Electrical Engineering, Cadi Ayyad University, Morocco
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Published 06-10-2024

Keywords

  • Product Customization,
  • Manufacturing
Creative Commons License

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

How to Cite

[1]
Dr. Aïcha Belhajjami, “The Application of Machine Learning in Enhancing Product Customization in American Manufacturing: Techniques and Real-World Examples”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 157–178, Oct. 2024, Accessed: Nov. 13, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/155

Abstract

The globalization of manufacturing industries has intensified competition among manufacturers. Consumers have grown accustomed to having the option to modify products, enacting customers’ ideas and providing customized products [1]. Product customization, defined as the production of products within preestablished ranges of parameters, such as function, geometry, and scheduling, enables manufacturers to offer particular products and services requested by customers.

Since the early 2000s, product customization has attracted the attention of industrial managers and academic scholars owing to its potential impact on both manufacturers and consumers. Product customization can create a win-win situation: customers receive products that provide better value and satisfaction, while manufacturers maintain order numbers, control productivity, and avoid overcapacity. In addition to each industry’s unique characteristics, there are also many universal challenges related to product customization. For instance, in product design, customization and standardization need to be balanced. In the manufacturing process, a trade-off between productivity and flexibility must be achieved. To solve these challenges, advanced technologies need to be adopted at lower levels.

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