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The integration of Artificial Intelligence in demand forecasting and inventory management in the United States
1 Department of Economics and Decision Sciences, Western Illinois University, Macomb, Illinois, US.
2 Department of Industrial Management, Texas A&M University, Kingsville, Texas, US.
3 Department of Economics, Obafemi Awolowo University, Ile-Ife, Nigeria.
Review
International Journal of Science and Research Archive, 2024, 13(01), 740–745.
Article DOI: 10.30574/ijsra.2024.13.1.1712
Publication history:
Received on 03 August 2024; revised on 12 September 2024; accepted on 14 September 2024
Abstract:
The integration of Artificial Intelligence in demand forecasting and inventory management in the United States has been examined in this paper. Demand forecasting and inventory management are two of critical areas in supply chain management, which is a veritable tool for promoting industrialization, manufacturing capabilities, and customers satisfaction. The use of AI in form of robotics, machine learning, deep learning, and predictive analytics, among others in all aspects of supply chain operations is gaining ground by the day. The integration of AI into the supply chain process can sustain multi-billion dollars trades in the United States, by reducing the cost of production and distribution, reducing human errors causing inaccurate demand forecasts, return shipment and cancellations of orders, etc. The challenges relating to the use of AI in demand forecasting and inventory management such as high cost of installation and maintenance, data privacy violations, requirement of skilled personnel, which are limited in global supply, and employees’ resistance to change were also identified. The outlook of relationship between Artificial Intelligence and supply chain management looks hopeful, brighter, and encouraging. This will be made possible by continuous development of AI capabilities and reducing the challenges of its widespread integration.
Keywords:
Demand Forecasting; Inventory Management; Artificial Intelligence; Supply Chain Management; Economic Growth; Efficiency.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
