Development and Accuracy Optimization of Machine Learning-Based Spare Parts Demand Forecasting Models

This study develops an intelligent spare parts demand forecasting system, leveraging machine learning and data-driven techniques, to address prevalent issues in traditional spare parts management, including inaccurate predictions, inventory imbalances, and resource wastage. Based on an analysis of core problems in spare parts management, various forecasting models and optimization methods are proposed, encompassing time series analysis, machine learning algorithms, and model ensemble strategies, significantly improving the accuracy and stability of spare parts demand forecasting. The system design includes modules for data management, model prediction, and results visualization. Through feature engineering, data augmentation, and parameter tuning, an efficient forecasting process is achieved, capable of flexibly addressing complex scenarios involving both high-frequency and low-frequency spare parts demand. Application evaluation demonstrates that the intelligent forecasting system has broad applicability in manufacturing, energy, and logistics sectors. It can reduce enterprise inventory costs, optimize supply chain management, and improve equipment utilization rates, while also minimizing spare parts wastage and promoting green production. In terms of economic and social benefits, the system not only enhances the resource allocation efficiency of enterprises but also provides significant support for industrial transformation by reducing downtime losses and supporting sustainable development.