## 2 Literature Review: Methods Used in the Spare Parts Supply System
In the field of forecasting, deep learning methods are rapidly developing and successfully applied, the study [15] considers the implementation of several deep learning algorithms for predicting the demand for vehicle spare parts, including fully connected networks, convolutional neural networks, and networks with long short-term memory. In [16], a method for predicting spare parts of discontinuous feature adaptation is proposed in terms of transfer learning. So, to predict the failure of aircraft parts that have the highest demand, an artificial neural network model is built, and it is first optimized, and then trained and tested using known sets of demand data for past periods [17]. Machine learning, multilevel generalization, and time series have been used as basic methods for demand forecasting in military logistics [18]. However, in some enterprises, due to insufficient data, if data is not systematically collected, predictive models will not work well and there will be difficulties with the data management system. It is impossible to manage inventory by placing an order strictly when the volume of spare parts falls below a given threshold, which is complicated by the uncertainty of demand and the uneven range of spare parts and depends on the volume, storage cost, and delivery cost. Parts in high demand are difficult to predict and, for this reason, the company Daimler-Benz in the 1970s refused to create warehouses for parts in frequent demand. The authors of [19] structured vehicle services into three different segments depending on the services provided, which made it possible to identify the risks of making wrong logistics decisions in each of the selected segments. Additive technologies can contribute to increasing the flexibility of spare parts supply [20]. Traditional prototyping is time-consuming and expensive as the product goes through more iterations. With 3D printing, it is possible to create high-quality prototypes in one day. The use of 3D printing is possible in the repair of technological equipment when replacing parts. Let us assume that the fastener on the manipulator is broken. When contracting the manufacturer for the purchase of one part, they would spend RUB 5000-10,000 and several days for delivery, if it is available for sale. The work of the 3D printer costs five hours and 500 rubles. The difference is obvious [21]. An example of the use of additive manufacturing in the automotive industry is the production of 100 headlight washer caps for a Lamborghini Gallardo, made by CRP Technologies using SLS processing of a material filled with carbon fibers. Additive technologies were used mainly for the manufacture of individual expensive parts, so now the focus is shifting to the mass production of partsand the production of spare parts [22]. Companies should pay attention to the flexibility of the supply chain by using additive technologies to increase its efficiency [23]. Additive manufacturing of automobiles will contribute to more efficient inventory management. A lot of research is devoted to improving work with suppliers. In reference [24], the problem is modeled using a probability tree and Markov chains to determine the optimal number of spare parts and suppliers. When selecting suppliers, an enterprise must have a supplier selection system. The reference [25] proposes a software module that will help select suppliers of spare parts based on their reliability from a logistical point of view. A problem tree was built to identify risks. The study [26] uses a Markov decision process to formulate a sequential decision problem that takes into account spare parts inventory information from two suppliers and takes into account which component will be replaced first in a two-component system. The right choice of suppliers facilitates strategic supply management to reduce the risk of purchasing spare parts. When choosing suppliers, many criteria must be taken into account, which is a difficult task to implement in a risky environment.