Predictive Intelligence in retail operations: AI-powered forecasting models for demand planning, customer behavior analysis, and supply chain optimization

The retail industry has undergone significant transformation with the integration of artificial intelligence (AI) and machine learning (ML) technologies. This study presents a comprehensive analysis of AI-powered forecasting models for retail operations, focusing on demand planning, customer behavior analysis, and supply chain optimization. Through the implementation of advanced predictive algorithms including Long Short-Term Memory (LSTM) networks, Random Forest, and XG Boost models, we demonstrate significant improvements in forecasting accuracy. Our findings reveal that AI-driven approaches achieve 23% better accuracy in demand forecasting, 31% improvement in customer behavior prediction, and 28% enhancement in supply chain optimization compared to traditional methods. The study utilizes real-world retail data from multiple sources to validate the effectiveness of these predictive intelligence systems. Results indicate that integrated AI solutions can reduce inventory costs by 15-20% while improving customer satisfaction scores by 18%. This research contributes to the growing body of knowledge on AI applications in retail and provides practical insights for industry practitioners.