Supply Chain Visualization and Optimization using Machine Learning

This research paper delves into the role of quantitative analysis in enhancing e-commerce supply chains. Leveraging machine learning techniques and a comprehensive e-commerce dataset from Kaggle, this study explores the nuanced art of demand forecasting. The application of the Prophet library in Python for predictive modeling serves as a cornerstone. Furthermore, this paper showcases the practical implications through a dynamic PowerBI dashboard, emphasizing the synthesis of predictive analytics and intuitive visualization. The findings underscore the accuracy of the predictive models and their impact on decision-making within e-commerce logistics. Through this research, a robust framework is unveiled, advancing the understanding of quantitative analysis in supply chains and enhancing operational efficiency and strategic decision-making in the dynamic realm of e-commerce.