Optimizing Supply Chain Operations using Predictive Analytics and Operations Research Techniques: A Comprehensive Analysis of Contemporary US Industrial Applications.

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@article{1709381,
      author = {Uchenna Evans-Anoruo},
      title = {Optimizing Supply Chain Operations using Predictive Analytics and Operations Research Techniques: A Comprehensive Analysis of Contemporary US Industrial Applications.},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {4},
      pages = {698-715},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709381.pdf},
      abstract = {Supply chain optimization has emerged as a critical competitive advantage in the modern American industrial landscape, where companies face unprecedented challenges including volatile demand patterns, geopolitical uncertainties, and increasing customer expectations for rapid delivery. This research examines the integration of predictive analytics and operations research techniques to enhance supply chain performance across multiple dimensions. Through comprehensive analysis of contemporary applications in the United States, this study demonstrates how advanced statistical methods, machine learning algorithms, and mathematical optimization models can significantly improve demand forecasting accuracy, reduce operational costs, and enhance delivery performance. The research synthesizes theoretical frameworks with practical implementations, providing insights into the transformative potential of data-driven supply chain management strategies.},
      keywords = {Supply Chain Optimization, Predictive Analytics, Operations Research, Machine Learning, Demand Forecasting, Inventory Management},
      month = {October},
  }