Machine Learning for Demand Forecasting in Manufacturing

Abstract

This research paper investigates the application of machine learning (ML) techniques in demand forecasting within the manufacturing sector. By analyzing case studies, practical examples, and comparative studies, we explore the effectiveness and challenges of ML-driven demand forecasting. The paper discusses various ML techniques, including regression models, time series forecasting methods, neural networks, and ensemble methods, highlighting their strengths and limitations. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are examined to assess forecasting accuracy. Additionally, challenges such as data quality, model interpretability, computational resources, and overfitting are discussed, along with recommendations for addressing these challenges. The paper concludes with recommendations for practitioners and suggestions for future research directions, emphasizing the importance of data quality improvement, model interpretability enhancement, and ethical considerations in ML-based demand forecasting.