The research is a review that covers various methods and techniques for spare parts management. It discusses traditional and modern forecasting methods, as well as different approaches to inventory classification and multi-criteria analysis. The paper identifies scientific gaps in the field of spare parts management and proposes new features to improve prediction accuracy. The study also highlights the importance of effective spare parts management in optimizing storage costs and ensuring the availability of raw materials and products. It addresses the challenge of disruptions in the production flow, which is a critical concern for companies, particularly those involved in manufacturing automobile engines. The research proposes a hybrid approach that combines both qualitative and quantitative methods, incorporating Vital–Essential–and Desirable (VED) analysis, and other techniques of spare parts classification methods. The goal is to identify and prioritize spare parts to be stocked efficiently. The aim of the study is to create a good classification that includes all possible criteria to ensure a good classification of spare parts, previous studies based their classification on one, two or three criteria at most, and this is not sufficient to calculate Re-order Point (ROP) and Economic Order Quantity (EOQ) with high accuracy based on changes in the market and supply chains.