Predictive Analytics for Enhancing Supply Chain Efficiency in the Consumer Goods Sector

Abstract

The consumer goods sector faces intense competition, dynamic demand patterns, and operational complexities that challenge supply chain efficiency. This manuscript explores the application of predictive analytics to optimize supply chain processes, focusing on demand forecasting, inventory management, and logistical planning. By integrating data-driven insights with traditional supply chain practices, organizations can reduce costs, improve service levels, and better manage risks. This study employs a mixed-methods approach, combining qualitative insights from recent literature with quantitative analysis using regression models. Results demonstrate that predictive analytics significantly enhances decision-making accuracy and operational efficiency. The discussion outlines the benefits, limitations, and practical considerations for implementation, providing a roadmap for practitioners aiming to integrate advanced analytics into their supply chains.