Research Article
Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results
Issue:
Volume 10, Issue 5, October 2025
Pages:
87-95
Received:
3 October 2025
Accepted:
14 October 2025
Published:
28 October 2025
Abstract: 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.
Abstract: 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 patter...
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Research Article
Local Time Stepping Scheme Using Structured Grids for Modelling of Shallow Water Flows
Myong Chol Ri*
,
Il Jang
Issue:
Volume 10, Issue 5, October 2025
Pages:
96-100
Received:
12 August 2025
Accepted:
9 September 2025
Published:
26 November 2025
DOI:
10.11648/j.ajmie.20251005.12
Downloads:
Views:
Abstract: Numerical simulations of shallow water flows are widely used to predict global flows such as flood flows in the sea, river and reservoir flows, especially floods due to heavy rainfall and dam break. In particular, reducing the simulation time by applying the Local Time Stepping (LTS) scheme is one way to improve the practical efficiency of numerical simulation. In this paper, we proposed LTS scheme using a structured grid for the numerical simulation of shallow water system. When modeling any terrain, rectangular grid cells are used to facilitate grid generation. To estimate the momentum flux at the grid cell boundaries, we applied the second-order spatial accuracy Godunov finite volume algorithm with Roe approximation solver using the MUSCL method. The LTS scheme is applied to shallow water flow problems with tsunami reflection pattern and its accuracy and efficiency are compared with the traditional global time step (GTS) method. Results show that, with no loss of accuracy, the new LTS algorithm achieves 59-67% CPU time reduction when compared to the GTS method. The proposed LTS scheme accurately reflects time varying water regimes and reflected waves in shallow water flows and can be used for numerical simulations of the three-dimensional shallow water flows with arbitrary topography to reduce the simulation time significantly.
Abstract: Numerical simulations of shallow water flows are widely used to predict global flows such as flood flows in the sea, river and reservoir flows, especially floods due to heavy rainfall and dam break. In particular, reducing the simulation time by applying the Local Time Stepping (LTS) scheme is one way to improve the practical efficiency of numerica...
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