Published Paper Details:

As global supply chains become increasingly complex, there is a rising dependence on predictive analytics for accurate demand forecasting. Traditional forecasting models based solely on historical data and statistical methods often do not capture volatility in the market, changing consumer behavior, and disruption in supply chains. Predictive analytics, powered by Machine Learning, artificial intelligence, and Big data analytics, allows organizations to enhance the accuracy of demand forecasting, streamline inventory management. The paper provides a literature review on synthetic predictive analytics methods, with emphasis on machine learning models, big data frameworks, and hybrid techniques for demand forecasting. Additionally, case studies of real-time applications across domains, including retail, e-commerce, and manufacturing, are explored to illustrate how predictive analytics can effectively reduce risks and improve supply chain performance. The research also looks into implementation challenges such as data quality, model interpretability and integration to existing enterprise systems. The results demonstrate the high-impact potential of predictive analytics on more extreme reactivity in contemporary SCM, and propose directions for future research to close the identified gaps