AI-Based Demand Sensing: Improving Forecast Accuracy in Supply Chains.

Abstract

In the rapidly evolving landscape of digital transformation, artificial intelligence (AI) stands at the forefront of enhancing supply chain operations. This paper explores the application of AI-based demand sensing in improving forecast accuracy within supply chains. Demand sensing utilizes real-time data to anticipate market demand with greater precision, and AI enhances this capability by processing vast datasets and uncovering patterns beyond human reach. By employing machine learning models, natural language processing, and advanced analytics, AI-based demand sensing provides deep insights into consumer behavior, market trends, and external factors influencing demand. Various AI techniques, such as neural networks, time-series analysis, and predictive analytics, are examined for their contributions to more accurate demand forecasts. The integration of diverse data sources, including social media, weather patterns, and economic indicators, is highlighted to show how these enrich AI models and offer a comprehensive view of demand drivers. The findings reveal significant benefits of AI-based demand sensing, including enhanced inventory management, reduced stockouts and overstocks, and increased agility in supply chain operations. Additionally, the paper addresses implementation challenges such as data quality, the need for specialized skills, and the importance of continuous model training and refinement. This study provides valuable insights for supply chain professionals and decision-makers, illustrating how AI can be leveraged to achieve superior forecast accuracy and operational efficiency.