The continuous operation of the mining industry directly depends on the availability of equipment. Optimizing the spare parts supply chain is a key factor in reducing operating costs and increasing productivity. [1]. However, the unstable demand for spare parts and long supply times make it difficult to implement an optimal resource management policy. For “Energy Resources” LLC, spare parts inventory accounts for a significant part of the operating costs. A shortage of spare parts causes technical downtime, while an excess of resources leads to the freezing of working capital [2]. Therefore, this study analyzed the current state of the company’s spare parts supply and sought opportunities to optimize resources using mathematical modeling. Mining spare parts research focuses on three major solutions in practice: warehouse optimization (ABC, SLP, EOQ, (S,s)), mathematical optimization/stochastic modeling (multi-echelon, robust optimization), and criticality-based classification + MCDM. [3]. The purpose of this study is to analyze the current state of spare parts supply in the mining transportation sector, identify the challenges, and suggest possible ways to optimize the supply process.

The normal operation of mining equipment directly depends on the availability of spare parts. The study analyzed the relationship between spare parts turnover, delivery time, stock level, and cost using supply chain theory, logistics optimization, and resource planning methodologies. By combining quantitative and qualitative data, it was possible to identify internal and external factors affecting the spare parts supply system.

The results of the study revealed the following main problems: poorly developed spare parts resource planning, non-optimal rotation classification, and high variability in supply time. In order to solve these problems, it was determined that it is necessary to implement measures such as refining supply planning, automating resource management, and establishing performance monitoring.

1. THEORETICAL OVERVIEW

In mining companies, spare parts supply is a strategic decision-making factor for ensuring production continuity, and failures in this system affect not only the efficiency of logistics, but also the productivity and cost structure of the entire organization. According to the main results of the conducted studies, the following basic system failures dominate the spare parts supply chain. [4].

First, the loss of resource estimation and uncertainty in demand-supply lead to a surplus or shortage of spare parts, which slows down the turnover of financial investments. When the demand forecasting model is poor, optimization based on reliability indicators such as MTBF and MTTR cannot be performed, and the planning efficiency is lost.

• Second, the long international supply cycle, combined with the size, weight, and transportation characteristics of mining equipment, further complicates the chain and increases the variability of supply time. As a result, the safety stock level is set higher than required, and the cost of warehousing tends to increase.
• Third, weak supplier reliability assessment poses a risk to the stability of supply, causing problems such as substandard parts, quality defects, and time discrepancies. Studies have shown that weak KPI systems that regularly evaluate supplier performance are a major cause of quality defects.
• Fourth, poor integration of warehouse management and information systems creates discrepancies between actual parts stocks and order information, creating conditions for making decisions based on outdated information. Research repeatedly shows that data errors increase when ERP, EAM, and CMMS systems are not fully integrated.
• Fifth, due to the characteristics of the mining industry, the backward procurement process and the complexity of coordination slow down the order coordination and increase the risk of production downtime.

The large number of procurement steps is the cause of the system to reduce the supply cycle. Finally, these problems together create high-cost risks such as reduced spare parts supply efficiency, reduced mining equipment service life, and increased unplanned downtime. According to the research level, it is necessary to integrate scientific methods such as mathematical modeling in demand forecasting, supplier risk management, system integration, and process innovation to optimize the mining spare parts supply system.

The METRIC model developed by Sherbrooke was the first to scientifically demonstrate the ability to determine the stock level of repairable parts in accordance with the system availability requirements. The peculiarity of this model is that it introduces a mathematical method for solving the relationship between the randomness of demand, the repair cycle, and the multi-level resource positions. The METRIC method is the basis of all advanced models in the industry today [5]. Researcher Muckstadt developed a scientific systematic theory of the relationship between the supply of parts and service level indicators.

His work includes multi-level system optimization, risk analysis, and low-demand spare parts classification methods. His research has been of practical significance by integrating the theory of service level control with resource policies and developing decision trees and algorithms that are suitable for the characteristics of low-demand and high-value spare parts [6]. The spare parts supply system in the mining industry is an important component that directly ensures the reliability of equipment, machinery, and production operations, and poor organization of the resource, order, and supply processes is the main cause of technical downtime, maintenance delays, and inefficient costs. In this context, the study of supply theory, resource management, and logistics optimization methodologies is the basis of research. A supply chain is a system that efficiently manages the flow of goods and services from source to consumer, including planning, warehouse organization, and transportation management. This theory suggests a methodology for optimizing inventory levels, order frequency, delivery time, and risk management. Based on this, the study identified weaknesses in the parts supply chain structure and identified opportunities for performance improvement.

The theory of stock classification allows for the optimal determination of stock levels by classifying them according to turnover, price, and frequency of demand. ABC classification is a widely used method that provides guidance for management by dividing spare parts into high, medium, and low turnover categories. Also, mathematical models such as Economic Order Quantity (EOQ) allow for the optimal calculation of order volume, lead time, and cost. The study used these methods to analyze spare parts turnover, stock levels, and order frequency. Demand forecasting is essential for ensuring efficient supply operations, and taking into account historical consumption, demand variability, and seasonality can reduce lead times and reduce the risk of surpluses and shortages.

This study used a combination of methods such as system analysis, quantitative data analysis, and qualitative analysis to analyze warehouse records, delivery time statistics, and work process reports to identify the main factors affecting spare parts performance. The optimization methods proposed in the study were practical, aimed at reducing delivery time, reducing excess inventory, and reducing cost risks, which is the novelty of the study, and it is believed that they can make a real contribution to improving the spare parts supply system in mining organizations, ensuring equipment continuity, and increasing production productivity. When managing spare parts resources, it is effective to adopt a differentiated policy based on demand frequency and value. The theoretical basis of the study focuses on determining the strategic importance of spare parts by combining ABC and XYZ classification methods. In this study, the Economic Order Quantity (EOQ) and safety stock models were used to minimize resource costs in accordance with the characteristics of Mongolian mining logistics. The EOQ model was used to calculate the optimal order quantity.

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