" A STUDY ON DATA-DRIVEN DECISION MODELS FOR OPTIMIZING INVENTORY MANAGEMENT AT RELIANCE SMART, NAGPUR

Abstract

Data-driven decision models have emerged as pivotal tools in streamlining inventory management, particularly in retail environments where efficient stock control and cost optimization are critical. This study explores the application of advanced analytical techniques in inventory management at Reliance Smart, Nagpur, focusing on the integration of predictive analytics and real-time data monitoring. The research emphasizes how data-driven models enhance decision-making by minimizing overstocking, mitigating stockouts, and improving demand forecasting accuracy. A systematic approach, combining quantitative data analysis with qualitative insights, uncovers the challenges and benefits of implementing these models. The study findings highlight a significant reduction in operational costs and enhanced customer satisfaction due to improved product availability. Additionally, it investigates the role of technological adoption, including the use of machine learning algorithms and inventory management software, in fostering a data-centric culture within the organization. Recommendations include scaling data-driven strategies to other operational areas to achieve holistic efficiency. The research contributes valuable insights for retail chains aiming to refine their inventory practices using advanced technologies and offers a roadmap for achieving sustainable growth through informed decision-making.