Role of Artificial Intelligence in Demand Forecasting Accuracy in the United States

William White ()

American Journal of Supply Chain Management, 2024, vol. 9, issue 3, 58 - 69

Abstract: Purpose: The aim of the study was to assess the role of artificial intelligence in demand forecasting accuracy in the United States. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: This study found that AI algorithms, particularly machine learning techniques, can analyze vast datasets and identify complex patterns that traditional forecasting methods often miss. For instance, AI systems can integrate external variables such as economic indicators, weather conditions, and consumer behavior to improve predictions. Research indicates that companies employing AI in their forecasting processes have reported up to a 30% improvement in accuracy compared to conventional methods. Furthermore, AI enables real-time adjustments to forecasts, allowing businesses to respond quickly to market changes and consumer demands, thereby optimizing inventory management and reducing costs. Overall, the adoption of AI in demand forecasting not only boosts accuracy but also enhances operational efficiency and strategic decision-making. Implications to Theory, Practice and Policy: Systems theory, theory of constraints (toc) and dynamic capabilities theory may be used to anchor future studies on assessing the role of artificial intelligence in demand forecasting accuracy in the United States. In practice, organizations should establish best practices for implementing artificial intelligence technologies in demand forecasting. Policymakers have a critical role in shaping the integration of artificial intelligence in demand forecasting through the development of comprehensive guidelines that promote responsible AI adoption across various industries.

Keywords: Artificial; Intelligence; Demand; Forecasting; Accuracy (search for similar items in EconPapers)
Date: 2024
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