Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-Driven Solutions for Enhancing Sustainability in U.S. Manufacturing Supply Chains

PDF
  • Dr. David Kim
Dr. David Kim
Associate Professor of Cybersecurity, Kookmin University, South Korea
Cover

Published 20-12-2023

Keywords

  • Sustainability,
  • U.S. Manufacturing,
  • Supply Chains
Creative Commons License

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

How to Cite

[1]
D. D. Kim, “AI-Driven Solutions for Enhancing Sustainability in U.S. Manufacturing Supply Chains”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 362–369, Dec. 2023, Accessed: Nov. 14, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/193

Abstract

AI has emerged as a promising tool for addressing sustainability challenges in manufacturing supply chains. The integration of AI technologies, such as machine learning and data analytics, holds the potential to optimize various components of the supply chain, including planning, sourcing, manufacturing, warehousing, distribution, and customer interface. [1] emphasizes that AI algorithms excel in leveraging large datasets from diverse sources, enabling machines to derive unique insights and perform tasks more efficiently than humans. This aligns with the scalability of AI within modern supply chains, which generate substantial volumes of data and operate within a network-based architecture. Furthermore, [2] highlight that the costs associated with AI applications in manufacturing should be viewed as long-term investments that promote environmental sustainability.

PDF

Downloads

Download data is not yet available.

References

  1. S. Kumari, “AI-Enhanced Agile Development for Digital Product Management: Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation”, Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 49–68, Mar. 2022
  2. Tamanampudi, Venkata Mohit. "A Data-Driven Approach to Incident Management: Enhancing DevOps Operations with Machine Learning-Based Root Cause Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 419-466.
  3. Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
  4. Tamanampudi, Venkata Mohit. "AI-Powered Continuous Deployment: Leveraging Machine Learning for Predictive Monitoring and Anomaly Detection in DevOps Environments." Hong Kong Journal of AI and Medicine 2.1 (2022): 37-77.
  5. Singh, Jaswinder. "Social Data Engineering: Leveraging User-Generated Content for Advanced Decision-Making and Predictive Analytics in Business and Public Policy." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 392-418.
  6. Tamanampudi, Venkata Mohit. "AI and NLP in Serverless DevOps: Enhancing Scalability and Performance through Intelligent Automation and Real-Time Insights." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 625-665.