Tree-Based Machine Learning Intermittent Demand Forecasting for Spare Parts in Electric Vehicle Manufacturing

As a crucial pillar industry in the country, the automotive industry continues to evolve with the increasing number of vehicles in operation, leading to a continual rise in the need for aftermarket parts and repair services. Fluctuations in automotive spare part requirements are influenced by various complex factors, which significantly impact production costs. The intermittent distribution of such requirements and strict limitations highlights the importance of automotive spare part management to enhance production efficiency and reduce costs. To improve demand forecasting accuracy, this study summarizes and synthesizes trends in automotive spare parts; proposes a tree-based machine learning forecasting model, based on a two-stage random forest (RF) structure that separately models demand occurrence probability and conditional demand size; and compares the outcomes with benchmarks to validate model effectiveness. The empirical study is conducted using an industrial dataset consisting of monthly demand records for approximately 2500 spare parts over a four-year period. This forecasting approach enables companies to rationalize inventory storage, ensure the quality of automotive repairs, and elevate service standards. Simultaneously, by improving the efficiency of inventory planning and allocation decisions, companies can enhance the quality of after-sales services, reduce inventory costs, and maximize the value of the automotive industry chain. Through reducing spare parts wastage and further lowering enterprise costs and industrial emissions, companies can achieve the goals of automotive supply chain resilience. Notably, this study focuses on automotive spare parts management and provides a feasible, reliable, and interpretable forecasting solution for automotive manufacturers to address intermittent demand challenges in spare parts management.