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oai:keep.lib.asu.edu:node-2000622025-03-20T17:14:44Zoai_pmh:alloai_pmh:repo_items

200062 https://hdl.handle.net/2286/R.2.N.200062 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2025-05 47 pages Bringas, Robert Wiedmer, Robert Chaturvedi, Hitendra Barrett, The Honors College Department of Supply Chain Management This thesis explores how Artificial Intelligence (AI) and AI-based forecasting are transforming supply chains by enhancing efficiency, adaptability, and predictability. Using a combination of literature review and industry survey, this study highlights the advantages of AI-based forecasting while identifying key challenges hindering widespread adoption. A decision framework is proposed to help organizations evaluate AI integration. The findings underscore AI’s growing role in supply chain management and its potential to reshape forecasting practices. While AI-based forecasting offers significant benefits, addressing implementation barriers is crucial to maximize its impact. This study identifies areas for further research and development, paving the way for more efficient, adaptable, and predictable supply chains. AI Forecasting AI-based Forecasting Neural Networks Supply Chain Supply Chain Management Forecasting in Supply Chain Management: How AI is Redefining Efficiency and Predictability