Innovative AI Solutions for Agriculture: Enhancing CropManagement and Yield

Authors

  • Laura Jeffrey Department of Computer Science, University of Mississippi Author
  • Revathi Bommu University of Illinois Springfield, One University Plaza, Springfield, IL 62703 Author

Keywords:

Dynamic inventory management, artificial intelligence, supply chain, demand forecasting, inventory optimization, real-time decision-making, supply chain resilience, USA market, stockouts, holding costs.

Abstract

Dynamic inventory management stands as a critical aspect of modern supply chain operations,
facilitating efficient inventory replenishment while minimizing holding costs and stockouts. In the
context of the USA's dynamic and diverse market landscape, traditional inventory management
approaches often fall short in adapting to fluctuating demand patterns and evolving consumer
preferences. This paper explores the transformative potential of AI-powered dynamic inventory
management systems in the USA, leveraging advanced algorithms for demand forecasting,
inventory optimization, and real-time decision-making. By harnessing the vast troves of data
generated across the supply chain, AI-driven inventory management systems enable companies to
anticipate demand fluctuations, optimize inventory levels, and enhance overall supply chain
resilience. Through case studies and industry examples, this paper elucidates the tangible benefits
of AI-powered inventory management in mitigating stockouts, reducing holding costs, and
improving customer satisfaction in the dynamic US market landscape. 

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Published

2024-05-11

How to Cite

Innovative AI Solutions for Agriculture: Enhancing CropManagement and Yield. (2024). International Journal of Advanced Engineering Technologies and Innovations, 1(3), 203-221. http://ijaeti.com/index.php/Journal/article/view/278

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