AI-Driven Demand Forecasting in Enterprise Retail Systems: Leveraging Predictive Analytics for Enhanced Supply Chain

Abstract

This article examines the transformative impact of artificial intelligence on demand forecasting systems within enterprise retail environments. It explores how advanced predictive analytics leverage machine learning algorithms to process vast quantities of data and generate more accurate forecasts compared to traditional methodologies. The article analyzes the evolution of demand forecasting techniques, implementation challenges, and quantifiable performance metrics across various retail sectors. Through case studies in fast fashion, grocery, and pharmaceutical retail, the article demonstrates how AI-driven systems enhance inventory optimization, markdown planning, supply chain orchestration, and omnichannel fulfillment. The article further addresses critical implementation challenges including data quality management, legacy system integration, organizational change processes, model maintenance requirements, and ethical considerations. By synthesizing empirical evidence from multiple retail environments, this article provides a comprehensive framework for understanding the current capabilities and future potential of AI-driven demand forecasting in retail enterprises.