Predictive Analytics Approaches for Improving Demand Forecasting Accuracy in E-Commerce Procurement Systems

Demand forecasting in e-commerce procurement systems is central to operational efficiency, customer satisfaction, and cost reduction. Accurate forecasting enables organizations to anticipate fluctuations in consumer demand, align procurement strategies, optimize inventory levels, and minimize supply chain risks. Traditional forecasting methods, while valuable, often fail to capture the complexities of modern e-commerce, where purchasing patterns are dynamic, influenced by seasonality, promotions, social media trends, and external shocks. Predictive analytics has emerged as a transformative approach, leveraging machine learning, statistical modelling, and big data analytics to enhance forecasting accuracy. By exploiting diverse data sources including transaction records, clickstream behaviour, demographic information, and external market signals predictive models enable organizations to move from reactive to proactive procurement management. This study conducts a comprehensive literature-based review of predictive analytics approaches in e-commerce demand forecasting up to 2020. It examines methodological developments, challenges, and applications across procurement systems, highlighting the integration of regression models, time-series forecasting, machine learning, and deep learning algorithms. The review identifies trends toward hybrid and ensemble models, the growing role of real-time analytics, and the challenges of scalability, data quality, and interpretability. The findings underscore that predictive analytics holds substantial potential in improving demand forecasting, but its successful adoption requires careful alignment with organizational goals, robust data governance, and cross-functional collaboration. The study provides a structured synthesis of existing scholarship and offers insights into the implications for e-commerce procurement systems seeking to enhance forecasting accuracy in volatile, data-rich environments.