Optimizing Supply Chain Operations using Predictive Analytics and Operations Research Techniques: A Comprehensive Analysis of Contemporary US Industrial Applications.
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
Citations
IRE Journals:
Uchenna Evans-Anoruo
"Optimizing Supply Chain Operations using Predictive Analytics and Operations Research Techniques: A Comprehensive Analysis of Contemporary US Industrial Applications." Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 698-715
IEEE:
Uchenna Evans-Anoruo
"Optimizing Supply Chain Operations using Predictive Analytics and Operations Research Techniques: A Comprehensive Analysis of Contemporary US Industrial Applications." Iconic Research And Engineering Journals, 7(4)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 9:
Issue 1Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12