A Review of Machine Learning Applications in Business Forecasting

Abstract

In the context of the digital economy, consumer demand, platform traffic, and supply chain environments are constantly changing. Accordingly, business forecasting has laid a critical foundation for enterprise inventory scheduling, marketing resource allocation and operational risk management. Compared with traditional statistical forecasting methods, such as ARIMA and exponential smoothing, machine learning can process multi-source, high-dimensional, and nonlinear information through big data and algorithms. It has gradually become an important research direction in business forecasting. This paper adopts systematic literature review and typical case analysis, to compare machine learning with traditional forecasting methods, explore its practical value in sales prediction, customer churn early warning and demand fluctuation evaluation, and sort out corresponding operational, compliance and technical risks as well as standardized governance requirements. The findings show that machine learning can improve forecasting accuracy and dynamic responsiveness while further supporting inventory and supply chain coordination, marketing decision-making, customer segmentation, and personalized recommendation. However, its effectiveness is also constrained by data quality, model interpretability, privacy compliance, and technical security. To solve the above problems, enterprises need to build a complete governance system covering standardized data management, scientific model supervision and manual human oversight. This paper contributes to understanding the transition of business forecasting from the traditional statistical paradigm to the intelligent forecasting paradigm and provides practical guidance for enterprises seeking to balance technological application, operational optimization, and risk control.