Data-Driven Inventory Optimization: Leveraging Advanced Analytics for Supply Chain Efficiency

Abstract

Effective inventory management is crucial for businesses to maintain operational efficiency and customer satisfaction while minimizing costs. This paper presents a comprehensive framework for inventory optimization using advanced data science techniques. By integrating machine learning, time series analysis, and optimization algorithms, we propose a robust approach to forecast demand, optimize stock levels, and enhance supply chain decision-making.Our methodology encompasses demand forecasting, multi-echelon inventory optimization, and dynamic reorder point calculation. The suggested framework is designed to lower inventory costs, decrease stock shortages, and enhance overall supply chain performance. This research provides valuable insights for businesses seeking to leverage data science for more effective inventory management in increasingly complex and uncertain market environments.