Optimizing Inventory Management through Machine Learning Algorithms: A Case Study in Supply Chain Optimization

Abstract

This paper presents a case study showcasing the application of machine learning algorithms to optimize inventory management within a supply chain context. Inventory management plays a critical role in balancing supply and demand, minimizing costs, and enhancing overall operational efficiency. Leveraging machine learning techniques offers a promising approach to address the complexities and uncertainties inherent in inventory management processes. The case study illustrates how machine learning algorithms are utilized to analyze historical sales data, forecast demand, and optimize inventory levels. By harnessing advanced predictive analytics, the case study demonstrates how organizations can improve inventory accuracy, reduce stockouts, and minimize excess inventory carrying costs. Furthermore, the case study explores future directions and emerging trends in leveraging machine learning for inventory management. Challenges such as data integration, scalability, and interpretability are addressed, along with recommendations for overcoming these obstacles and maximizing the potential of machine learning in supply chain optimization.