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

Polvonov Abdujalil Sattorovich; Abdusattorov Nodirjon Abdujalil o‘g‘li; Odilov Jakhongir Anvarjon o‘g‘li; Sulaymonov Dostonbek Salohiddin o‘g‘li

doi:doi:10.11648/j.ajmie.20251005.11

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

Polvonov Abdujalil Sattorovich

, Abdusattorov Nodirjon Abdujalil o‘g‘li

, Odilov Jakhongir Anvarjon o‘g‘li

, Sulaymonov Dostonbek Salohiddin o‘g‘li

Published in American Journal of Mechanical and Industrial Engineering (Volume 10, Issue 5)

Received: 3 October 2025 Accepted: 14 October 2025 Published: 28 October 2025

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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.

Published in	American Journal of Mechanical and Industrial Engineering (Volume 10, Issue 5)
DOI	10.11648/j.ajmie.20251005.11
Page(s)	87-95
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Car Service, Spare Parts Management, Regression Models, Poisson Distribution, Probability and Statistical Analysis, Maintenance and Repair Processes

References

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[3]	Musajonov M. Z. Fundamentals of Designing Auto Service Enterprises. - Tashkent: “Tamaddun” Publishing House, 2017.
[4]	Polvonov A. S. Fundamentals of Designing Auto Service Enterprises: Textbook for Higher Education Institutions. - Namangan: “Usmon Nosir Media”, 2023. - 283 p.
[5]	Grishin A. S. Development of a Methodology for Forecasting the Demand of Auto Service Enterprises for Spare Parts: Dissertation for the Degree of Candidate of Technical Sciences: 05.22.10 - Moscow, 2005.
[6]	Bugrimov V. A. Modeling the Flow of Spare Parts Orders in Auto Service / V. A. Bugrimov, A. V. Kondratyev, V. I. Sarbaev // Efficiency of Technical Operation and Auto Service of Transport and Technological Machines, 3rd International Scientific Conference of Students and Young Scientists. - Saratov, 2017. - P. 14-19.
[7]	Polvonov A. S., Odilov J. A. Avtoservis korxonalarida ehtiyot qismlarga bo‘lgan talab darajasini o‘rganish. Spectrum Journal of Innovation, Reforms and Development. Veb-sayt: www.sjird.journalspark.org
[8]	A. S. Polvonov, D. S. Shotmonov, N. A. Abdusattorov, J. A. Odilov, D. S. Sulaymonov. Matematik modellardan foydalanib ehtiyot qismlarga bo‘lgan talabni hisoblash natijalarini taqqoslash. "Iqtisodiyot va Jamiyat" №7(134), 2025. Veb-sayt: www.iupr.ru
[9]	Polvonov A. S. Avtoservis korxonalarida ehtiyot qismlarning aylanma fondi hajmini optimallashtirish masalasini hal qilish. Ilmiy jurnal “Mexanika va Texnologiya”. Namangan, 2024, №3, maxsus son. B. 80-85.
[10]	Polvonov A. S., Abdusattorov N., Soataliev D. Avtoservis korxonalarini joylashtirish masalasini simulyatsion model dasturi asosida amalga oshirish. Namangan Davlat Qurilish va Injenerlik Instituti, “Mexanika va Texnologiya” ilmiy jurnali, №1 (10), 2023.
[11]	Sharipov K., Polvonov A., Abdusattorov N., Theoretical aspects of territorial location modeling of automobile service enterprises. The Seybold REPORT ISSN 1533-9211 https://doi.org/10.5281/zenodo.6969377 V 1 7. I 0 8. 2022.
[12]	Polvonov, I. Mukhamadov, D. Soataliyev. Study of the Ultimate Stress, Relative Elongation, and Specific Work at the Rupture of Vilad-11 Polyurethane Adhesive. Namangan State University of Engineering, Journal of Mechanics and Technology, No. 1 (6), 2022.
[13]	Burger, N., Laachachi, A., Ferriol, M., Lutz, M., Toniazzo, V., Ruch, D. Review of thermal conductivity in composites: Mechanisms, parameters and theory. Prog. Polym. Sci. 2016- обзор механизмов теплопроводности композитов.
[14]	Wang, J. et al. Development and Perspectives of Thermally Conductive Polymer Composites. MDPI (2022) — Analysis of the Current State and Prospective Directions in the Creation of Polymer Composites with High Thermal Conductivity. MDPI.

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Sattorovich, P. A., o‘g‘li, A. N. A., o‘g‘li, O. J. A., o‘g‘li, S. D. S. (2025). Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results. American Journal of Mechanical and Industrial Engineering, 10(5), 87-95. https://doi.org/10.11648/j.ajmie.20251005.11

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ACS Style

Sattorovich, P. A.; o‘g‘li, A. N. A.; o‘g‘li, O. J. A.; o‘g‘li, S. D. S. Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results. Am. J. Mech. Ind. Eng. 2025, 10(5), 87-95. doi: 10.11648/j.ajmie.20251005.11

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AMA Style

Sattorovich PA, o‘g‘li ANA, o‘g‘li OJA, o‘g‘li SDS. Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results. Am J Mech Ind Eng. 2025;10(5):87-95. doi: 10.11648/j.ajmie.20251005.11

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@article{10.11648/j.ajmie.20251005.11,
  author = {Polvonov Abdujalil Sattorovich and Abdusattorov Nodirjon Abdujalil o‘g‘li and Odilov Jakhongir Anvarjon o‘g‘li and Sulaymonov Dostonbek Salohiddin o‘g‘li},
  title = {Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results
},
  journal = {American Journal of Mechanical and Industrial Engineering},
  volume = {10},
  number = {5},
  pages = {87-95},
  doi = {10.11648/j.ajmie.20251005.11},
  url = {https://doi.org/10.11648/j.ajmie.20251005.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20251005.11},
  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.
},
 year = {2025}
}

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TY  - JOUR
T1  - Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results

AU  - Polvonov Abdujalil Sattorovich
AU  - Abdusattorov Nodirjon Abdujalil o‘g‘li
AU  - Odilov Jakhongir Anvarjon o‘g‘li
AU  - Sulaymonov Dostonbek Salohiddin o‘g‘li
Y1  - 2025/10/28
PY  - 2025
N1  - https://doi.org/10.11648/j.ajmie.20251005.11
DO  - 10.11648/j.ajmie.20251005.11
T2  - American Journal of Mechanical and Industrial Engineering
JF  - American Journal of Mechanical and Industrial Engineering
JO  - American Journal of Mechanical and Industrial Engineering
SP  - 87
EP  - 95
PB  - Science Publishing Group
SN  - 2575-6060
UR  - https://doi.org/10.11648/j.ajmie.20251005.11
AB  - 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.

VL  - 10
IS  - 5
ER  -

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Author Information

Polvonov Abdujalil Sattorovich

Transport Faculty, Namangan State Technical University, Namangan, Uzbekistan

Contact Email

http://orcid.org/0009-0007-8473-2194
Abdusattorov Nodirjon Abdujalil o‘g‘li

Transport Faculty, Namangan State Technical University, Namangan, Uzbekistan

Contact Email

http://orcid.org/0009-0006-5496-9708
Odilov Jakhongir Anvarjon o‘g‘li

Mechanics and Mechanical Engineering Faculty, Fergana State Technical University, Fergana, Uzbekistan

Contact Email

http://orcid.org/0000-0002-0229-5354
Sulaymonov Dostonbek Salohiddin o‘g‘li

Transport Faculty, Namangan State Technical University, Namangan, Uzbekistan

Contact Email

http://orcid.org/0000-0002-8000-4874

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Plain Text BibTeX RIS

APA Style

Sattorovich, P. A., o‘g‘li, A. N. A., o‘g‘li, O. J. A., o‘g‘li, S. D. S. (2025). Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results. American Journal of Mechanical and Industrial Engineering, 10(5), 87-95. https://doi.org/10.11648/j.ajmie.20251005.11

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ACS Style

Sattorovich, P. A.; o‘g‘li, A. N. A.; o‘g‘li, O. J. A.; o‘g‘li, S. D. S. Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results. Am. J. Mech. Ind. Eng. 2025, 10(5), 87-95. doi: 10.11648/j.ajmie.20251005.11

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AMA Style

Sattorovich PA, o‘g‘li ANA, o‘g‘li OJA, o‘g‘li SDS. Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results. Am J Mech Ind Eng. 2025;10(5):87-95. doi: 10.11648/j.ajmie.20251005.11

Copy | Download

@article{10.11648/j.ajmie.20251005.11,
  author = {Polvonov Abdujalil Sattorovich and Abdusattorov Nodirjon Abdujalil o‘g‘li and Odilov Jakhongir Anvarjon o‘g‘li and Sulaymonov Dostonbek Salohiddin o‘g‘li},
  title = {Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results
},
  journal = {American Journal of Mechanical and Industrial Engineering},
  volume = {10},
  number = {5},
  pages = {87-95},
  doi = {10.11648/j.ajmie.20251005.11},
  url = {https://doi.org/10.11648/j.ajmie.20251005.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20251005.11},
  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.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Mathematical Models for Forecasting Spare Parts Demand in Car Service Enterprises and Comparative Analysis of Results

AU  - Polvonov Abdujalil Sattorovich
AU  - Abdusattorov Nodirjon Abdujalil o‘g‘li
AU  - Odilov Jakhongir Anvarjon o‘g‘li
AU  - Sulaymonov Dostonbek Salohiddin o‘g‘li
Y1  - 2025/10/28
PY  - 2025
N1  - https://doi.org/10.11648/j.ajmie.20251005.11
DO  - 10.11648/j.ajmie.20251005.11
T2  - American Journal of Mechanical and Industrial Engineering
JF  - American Journal of Mechanical and Industrial Engineering
JO  - American Journal of Mechanical and Industrial Engineering
SP  - 87
EP  - 95
PB  - Science Publishing Group
SN  - 2575-6060
UR  - https://doi.org/10.11648/j.ajmie.20251005.11
AB  - 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.

VL  - 10
IS  - 5
ER  -

Copy | Download