Inventory management and demand planning are critical components of an efficient supply chain. This paper explores the role of data-driven strategies McKinsey & Co. (2020) [2] in improving inventory management and demand planning accuracy. The study examines advanced forecasting models, machine learning techniques, and real-time data integration to enhance decision-making and reduce inventory holding costs. Case studies from leading organizations are presented to highlight successful implementation and measurable performance improvements. The paper also addresses the role of technological innovations and the impact of global supply chain disruptions on inventory management and demand forecasting. Furthermore, it explores how real-time data, automation, and AI-based models enable greater inventory control and responsiveness, leading to improved supply chain agility.