AI - Driven Supply Chain Optimization: Enhancing Inventory Management, Demand Forecasting, and Logistics within ERP Systems

Abstract

This research paper delves into the realm of AI -driven supply chain optimization within ERP systems, with a specific focus on inventory management, demand forecasting, and logistics operations in the Indian context. The primary objective is to explore the extent to which integrating AI into ERP systems can enhance supply chain performance metrics. Employing a quantitative research design, the study gathered data through a comprehensive survey targeting supply chain managers and IT professionals across various industries in India. Structural Equation Modeling (SEM) was utilized to analyze the collected data, providing a robust framework to assess the relationships between AI integration and supply chain performance indicators. Key findings from the SEM analysis reveal a significant positive impact of AI integration on inventory management efficiency, demand forecasting accuracy, and logistics operations. The results indicate that higher levels of AI integration correlate with substantial improvements in these supply chain performance metrics, underscoring the scalability of AI benefits in supply chain optimization. The study fills a critical gap in the literature by offering empirical evidence on the effectiveness of AI -driven ERP systems in the Indian supply chain context, thereby contributing actionable insights for businesses operating in or with India. These findings have broad implications for supply chain management practices, suggesting that businesses can achieve considerable gains in efficiency and effectiveness by adopting AI -driven ERP solutions. The research underscores the transformative potential of AI in supply chain optimization and highlights the need for businesses to embrace these technologies to remain competitive in the rapidly evolving global market.