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

@article{1709913,
      author = {Omotolani David Lawal, Oluwatumininu Ajayi},
      title = {Optimizing Supply Chain Operations Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis of the American Industrial Landscape},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {5},
      pages = {211-222},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709913.pdf},
      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},
      month = {November},
  }