Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results

This study comprehensively examines the challenges of meeting the spare parts demand in car service enterprises. The research addresses key aspects such as maintaining optimal spare parts inventories, organizing efficient storage in warehouses, and improving the processes of ordering, purchasing, and delivering spare parts. To analyze demand patterns, the Poisson distribution is applied, and regression models are used to identify the factors influencing spare parts consumption. The study highlights the importance of developing a multiple regression model to determine the degree of interrelation between these influencing factors. Variables with pair correlation coefficients below the specified significance level are excluded to enhance the model’s accuracy and reliability. In addition, the potential of adaptive forecasting models based on the moving average method is explored to predict future spare parts demand effectively. A comparative analysis of the results obtained from different mathematical models demonstrates that the proposed approach provides a more accurate and reliable estimation of spare parts demand for car service enterprises. The findings offer practical guidance for inventory management, helping enterprises maintain sufficient stock levels while minimizing storage costs and operational inefficiencies. By combining statistical modeling with adaptive forecasting techniques, this study provides a comprehensive framework for predicting spare parts demand and supporting decision-making in car service enterprises. The approach contributes to improved operational efficiency, reduced risk of stockouts, and better alignment of inventory with actual service requirements.

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