Tel: +998977838464
Search

№ 3 (2026)
Marketing

Научные статьи

FARMATSEVTIKA BOZORIDA TALAB VA TAKLIFNIPROGNOZLASHDA SUNʼIY INTELLEKT ALGORITMLARIDANFOYDALANISH

DOI 10.67668/mj/2026iss3/874

Опубликован 2026-04-03

Авторы
Xomidov Soxibmalik Olimovich
PDF (узбекский) Google Scholar Google Scholar

Аннотация

Ushbu ilmiy maqolada farmatsevtika bozorida talab va taklifni prognozlashda
sunʼiy intellekt (SI) algoritmlaridan foydalanish masalalari tadqiq etilgan. Maqsad:
farmatsevtika sohasida SI asosidagi prognozlash modellarini qiyosiy tahlil qilish va
Oʻzbekiston farmatsevtika bozori uchun eng samarali algoritmni aniqlash.
Metodologiya: mashinali oʻqitish (ML) algoritmlari – LSTM, Random Forest,
XGBoost, Prophet – 2018–2024 yillar uchun Oʻzbekiston farmatsevtika bozori
maʼlumotlari asosida sinab koʻrildi. RMSE, MAE va MAPE koʻrsatkichlari asosida
algoritmlar qiyosiy baholandi. Natijalar: LSTM neyron tarmogʻi eng yuqori aniqlikni
koʻrsatdi (MAPE=4.2%), XGBoost esa tezligi va talqin qilish qulayligi bilan ajralib
turdi. Xulosa: Gibrid SI-modellari (LSTM+XGBoost) Oʻzbekiston farmatsevtika
bozorida talab-taklifni real vaqtda prognozlash uchun optimal yechim hisoblanadi.

Ключевые слова

Array

Библиографические ссылки

  1. 1. Aamer, A., Eka Putri, L., Eka Pratama, I., & Antara, I.N.Y. (2023). Data
  2. analytics for supply chain demand forecasting: A pharmaceutical case study.
  3. International Journal of Supply Chain Management, 12(1), 48-64.
  4. 2. Babich, V., & Kouvelis, P. (2018). Introduction to the special issue on research
  5. at the interface of finance, operations, and risk management. Manufacturing & Service Operations Management, 20(1), 1-10.
  6. 3. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system.
  7. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge
  8. Discovery and Data Mining, 785-794.
  9. 4. Huang, T., Fildes, R., & Soopramanien, D. (2021). Forecasting demand for
  10. online grocery retail: Insights from both statistical and machine learning approaches. European Journal of Operational Research, 294(2), 615-631.
  11. 5. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T.Y.
  12. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
  13. 6. Kumar, A., & Bhatia, R. (2022). AI-based demand forecasting in
  14. pharmaceutical supply chains: An empirical study. Journal of Business Logistics,
  15. 43(4), 512-538.
  16. 7. Syntetos, A.A., Boylan, J.E., & Croston, J.D. (2005). On the categorization of
  17. demand patterns. Journal of the Operational Research Society, 56(5), 495-503.
  18. 8. Taylor, S.J., & Letham, B. (2018). Forecasting at scale. The American
  19. Statistician, 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080
  20. 9. Williams, B.D., & Kolassa, S. (2020). Supply chain forecasting: a
  21. pharmaceutical case study. International Journal of Forecasting, 36(3), 1030-1049.
  22. 10. Zhang, Q., Li, H., Li, Z., & Wang, J. (2023). Deep learning for pharmaceutical
  23. demand forecasting in China. Health Informatics Journal, 29(1), 1-18.
  24. https://doi.org/10.1177/14604582231157439
  25. 11. Oʻzbekiston Respublikasi Sogʻliqni saqlash vazirligi (2024). Oʻzbekiston
  26. farmatsevtika bozori 2024-yil hisoboti. Toshkent: SSV nashriyoti.
  27. 12. Oʻzfarmsanoat Uyushmasi (2024). Oʻzbekiston farmatsevtika sanoatining
  28. 2018–2024 yillardagi rivojlanish koʻrsatkichlari. Toshkent.
  29. 13. Oʻzbekiston Respublikasi Prezidentining 2025-yil 28-yanvar PF-13-son
  30. “Farmatsevtika tarmogʻini jadal rivojlantirishga oid qoʻshimcha chora-tadbirlar
  31. toʻgʻrisida”gi Farmoni https://lex.uz/uz/docs/-7345588
  32. 14. Statista (2024). Global pharmaceutical market size from 2001 to 2030.
  33. Retrieved from https://www.statista.com/statistics/263102/pharmaceutical-marketworldwide-revenue-since-2001
  34. 15. World Health Organization (2024). Global pharmaceutical market data and
  35. pricing. WHO Geneva.

Скачивания

Данные скачивания пока недоступны.