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Unleashing the Power of Generative AI and RAG Agents in Supply Chain Management: A Futuristic Perspective
Subject area: Science,Engineering and Technology · Area of research: Generative AI
Abstract
Supply chain management (SCM) plays a critical role in today's complex business environment, and the advancements in artificial intelligence (AI) have the potential to revolutionize SCM practices. This research article explores the untapped potential of generative AI and RAG (Retrieval-Augmented Generation) agents in SCM, presenting a futuristic perspective on their application. The research begins with an overview of SCM and the growing significance of AI in transforming supply chain operations. It then introduces the concepts of generative AI and RAG agents, highlighting their unique capabilities and potential benefits in SCM. A comprehensive literature review examines existing research on AI in SCM and explores the applications of generative AI and RAG agents in other domains. The review identifies research gaps and opportunities for the utilization of generative AI and RAG agents specifically in SCM. The methodology section outlines the research approach, including data collection methods and the implementation details of generative AI and RAG agents. Evaluation metrics are explained to assess the effectiveness and performance of these technologies in SCM. The article presents practical applications of generative AI and RAG agents in SCM, focusing on their roles in demand forecasting, inventory management, supply chain operations, and real-time decision-making. Case studies and experimental results are provided to demonstrate their potential impact on SCM efficiency and customer satisfaction. The results and analysis section presents the findings of the experiments conducted, analyzing both quantitative and qualitative aspects. A comparison with existing approaches in SCM further highlights the unique advantages of generative AI and RAG agents.The discussion section interprets the results, discusses the implications for SCM, and addresses the limitations and challenges associated with the adoption of generative AI and RAG agents in SCM. It also identifies future research directions and opportunities for further exploration. In conclusion, this research article sheds light on the transformative power of generative AI and RAG agents in SCM. It contributes to the field by providing a futuristic perspective on their application, offering recommendations for practitioners and policymakers. The article concludes by emphasizing the promising future of generative AI and RAG agents in shaping the SCM landscape.
Keywords
Generative AI, RAG agent, Supply chain management, Artificial intelligence, Futuristic Technology
References
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How to cite this paper
@article{1704588,
author = {Venkat Sharma Gaddala},
title = {Unleashing the Power of Generative AI and RAG Agents in Supply Chain Management: A Futuristic Perspective},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {1411-1417},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704588.pdf},
abstract = {Supply chain management (SCM) plays a critical role in today's complex business environment, and the advancements in artificial intelligence (AI) have the potential to revolutionize SCM practices. This research article explores the untapped potential of generative AI and RAG (Retrieval-Augmented Generation) agents in SCM, presenting a futuristic perspective on their application. The research begins with an overview of SCM and the growing significance of AI in transforming supply chain operations. It then introduces the concepts of generative AI and RAG agents, highlighting their unique capabilities and potential benefits in SCM. A comprehensive literature review examines existing research on AI in SCM and explores the applications of generative AI and RAG agents in other domains. The review identifies research gaps and opportunities for the utilization of generative AI and RAG agents specifically in SCM. The methodology section outlines the research approach, including data collection methods and the implementation details of generative AI and RAG agents. Evaluation metrics are explained to assess the effectiveness and performance of these technologies in SCM. The article presents practical applications of generative AI and RAG agents in SCM, focusing on their roles in demand forecasting, inventory management, supply chain operations, and real-time decision-making. Case studies and experimental results are provided to demonstrate their potential impact on SCM efficiency and customer satisfaction. The results and analysis section presents the findings of the experiments conducted, analyzing both quantitative and qualitative aspects. A comparison with existing approaches in SCM further highlights the unique advantages of generative AI and RAG agents.The discussion section interprets the results, discusses the implications for SCM, and addresses the limitations and challenges associated with the adoption of generative AI and RAG agents in SCM. It also identifies future research directions and opportunities for further exploration. In conclusion, this research article sheds light on the transformative power of generative AI and RAG agents in SCM. It contributes to the field by providing a futuristic perspective on their application, offering recommendations for practitioners and policymakers. The article concludes by emphasizing the promising future of generative AI and RAG agents in shaping the SCM landscape.},
keywords = {Generative AI, RAG agent, Supply chain management, Artificial intelligence, Futuristic Technology},
month = {June},
}