AI-Enhanced Supply Chain Visibility in Boosting American Manufacturing Competitiveness
Keywords:
Supply Chain Visibility, American ManufacturingAbstract
The introduction section of this essay serves as a foundational framework for exploring the impact of AI-enhanced supply chain visibility on American manufacturing competitiveness. It provides an initial understanding of the study's key components, objectives, background, significance, problem statement, and purpose. The significance of AI in supply chain management is underscored by its ability to capitalize on large datasets from various sources, enabling machines to derive unique insights and perform tasks more efficiently than humans [1]. The scalability of AI within modern supply chains is highlighted, emphasizing the untapped potential value due to legacy SCM tools being overstrained by the volume, velocity, and variety of data characterizing modern supply chains.
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References
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
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.
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.
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.
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.
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.
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