AI-Enabled Market Forecasting: Improving Demand Prediction in Volatile Environments

Abstract

This study examines how artificial intelligence based demand forecasting can enhance decision making in increasingly volatile product markets. Shorter product life cycles, intensive promotions, technological disruption and supply chain shocks make small forecasting errors translate into stockouts, excess inventory and financial underperformance. Traditional statistical time series models perform well only under stable conditions and struggle when demand is intermittent, promotion driven or influenced by fast moving external variables. A systematic literature review of empirical and review studies on retail, electronic commerce and supply chain contexts synthesises evidence on machine learning and deep learning applications for sales and demand forecasting. The findings show that algorithms such as random forests, gradient boosting, support vector regression, recurrent neural networks and hybrid deep learning architectures generally outperform conventional benchmarks, especially when high frequency market and supply chain signals are incorporated. However, the review also highlights persistent challenges related to data quality, feature drift, model transparency and integration into existing sales and operations planning processes.