Implementation of Bigdata Techniques in Sales Forecasting: Evidence from Retailing and E-commerce

Sales forecasting is a key part of business operations, providing essential insights for inventory management, financial planning, and strategic decision-making. With the advent of big data, traditional forecasting methods have evolved, leveraging advanced machine learning and different deep learning models to increase accuracy and efficiency. This paper reviews recent research on the use of big data techniques in sales forecasting, focusing on diverse industries such as retail, e-commerce, and automotive. Through an analysis of case studies and research findings, this study emphasizes the effectiveness of machine learning models like XGBoost, Random Forests, and RNNs in improving forecast accuracy in the context of big data. The review also addresses the challenges and limitations of current methodologies, offering insights into future research directions. The significance of this research lies in its comprehensive overview of the state-of-the-art in sales forecasting, providing a valuable resource for researchers and practitioners aiming to optimize business strategies through big data.