APPLICATIONS OF MACHINE LEARNING IN FORECASTING OPERATIONAL COSTS

Resumo

Machine learning (ML) has emerged as a transformative tool for forecasting operational costs across industries, offering data-driven insights that enhance efficiency, risk management, and decision-making. Unlike traditional statistical models, ML approaches can capture nonlinear relationships, complex variable interactions, and hidden patterns in high-dimensional datasets. This paper discusses the main applications of ML in operational cost forecasting, focusing on predictive maintenance, energy cost prediction, hybrid modeling, and uncertainty quantification. It emphasizes the role of data quality, feature engineering, model interpretability, and governance in ensuring reliable deployment. Empirical evidence and real-world studies demonstrate that hybrid and ensemble approaches outperform single-model solutions, reducing forecasting errors and improving business adaptability. The study concludes that integrating ML with traditional forecasting techniques provides a pragmatic and effective strategy for managing financial uncertainty and optimizing resource allocation in dynamic operational environments.