Optimizing Supply Chain Operations Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis of the American Industrial Landscape

• Author(s): Omotolani David Lawal ; Oluwatumininu Ajayi
• Paper ID: 1709913
• Page: 211-222
• Published Date: 30-11-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 5 November-2019

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into supply chain operations has emerged as a transformative force in American industry. This paper examines the current state of AI-driven supply chain optimization, analyzing implementation strategies, performance metrics, and economic impacts across various sectors in the United States. Through comprehensive analysis of industry data and case studies spanning 2015-2019, this research demonstrates that organizations implementing AI and ML technologies achieve average cost reductions of 15-25% while improving delivery performance by 20-35%. The study reveals that predictive analytics, demand forecasting, and autonomous logistics systems represent the most impactful applications of these technologies in contemporary supply chain management.

Keywords

Supply Chain Management, Artificial Intelligence, Machine Learning, Optimization, Industrial Automation, Predictive Analytics

Citations

IRE Journals:
Omotolani David Lawal , Oluwatumininu Ajayi "Optimizing Supply Chain Operations Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis of the American Industrial Landscape" Iconic Research And Engineering Journals Volume 3 Issue 5 2019 Page 211-222

IEEE:
Omotolani David Lawal , Oluwatumininu Ajayi "Optimizing Supply Chain Operations Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis of the American Industrial Landscape" Iconic Research And Engineering Journals, 3(5)