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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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AI-Driven ERP Evolution: Enhancing Supply Chain Resilience with Neural Networks and Predictive LSTM Models

Authors: Vinay Singh;

AI-Driven ERP Evolution: Enhancing Supply Chain Resilience with Neural Networks and Predictive LSTM Models

Abstract

Major advances in enterprise resource planning (ERP) systems have come from the fast development of artificial intelligence (AI) technology, therefore enabling businesses to streamline their operations, enhance decision-making, and increase supply chain efficiency. This article looks at how integrating artificial intelligence with ERP systems can enable companies to improve procurement procedures and enhance supply chain efficiency. Using artificial intelligence technologies—including machine learning, natural language processing, robotic process automation (RPA), and predictive analytics—one may automate repetitive operations, streamline processes, and provide insightful analysis for proactive decision-making. Emphasizing demand forecasting, inventory management, Sourcing, supplier management, and process automation—among other AI-driven capabilities—the study shows how these developments enable companies to reach smarter, data-driven decisions The article also looks at the benefits and difficulties of using artificial intelligence in ERP including data privacy issues and complicated integration. Emphasizing the transforming power of artificial intelligence to revolutionize organizational efficiency in the digital age, the study ends with recommendations and real-world examples for companies trying to use AI for improved ERP functionality.

Keywords

Machine Learning, Artificial Intelligence, Oracle EBS, Oracle Fusion Cloud, ERP Systems

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green