AI-Driven Demand and Supply Forecasting Models for Enhanced Sales Performance Management: A Case Study of a Four-Zone Structure in the United States

Abstract

This study investigates the application of AI-driven demand and supply forecasting models to enhance sales performance management within a four-zone structure in the United States. With increasing market volatility and regional variability, traditional forecasting methods often fail to capture real-time dynamics and cross-zone dependencies. Leveraging machine learning algorithms and data analytics, this case study explores the implementation of predictive models tailored to zone-specific characteristics, seasonal trends, and historical sales data. The research evaluates model accuracy, adaptability, and impact on decision-making efficiency, inventory optimization, and revenue growth. Findings demonstrate that AI-enhanced forecasting significantly improves planning precision, reduces stockouts and overstocks, and aligns sales strategies with localized demand patterns. This paper contributes practical insights for businesses seeking to adopt intelligent forecasting systems in multi-regional operations.