KICHIK BIZNES SUBYEKTLARIDA SUNʼIY INTELLEKT ASOSIDA ISHLAB CHIQARISHDAGI NUQSONLARNI KAMAYTIRISHNING IQTISODIY SAMARADORLIGI

KICHIK BIZNES SUBYEKTLARIDA SUNʼIY INTELLEKT ASOSIDA ISHLAB CHIQARISHDAGI NUQSONLARNI KAMAYTIRISHNING IQTISODIY SAMARADORLIGI

Authors

  • Sodiqova Dilnoza Jumanazarovna Muhammad al-Xorazmiy nomidagi, Toshkent axborot texnologiyalari universiteti, t.f.n., PhD

Keywords:

sunʼiy intellekt, kichik biznes, nuqsonlarni aniqlash, sifat xarajatlari, past sifat xarajati, mashinaviy koʻrish, qoplanish muddati, sifat darvozasi.

Abstract

Maqolada kichik biznes subyektlarida sunʼiy intellekt asosida ishlab chiqarishdagi nuqsonlarni kamaytirishning iqtisodiy samaradorligi sifat xarajatlari nazariyasi doirasida oʻrganilgan. Oldini olish, baholash, ichki va tashqi nosozlik xarajatlari tasnifi, Taguchi yoʻqotish funksiyasi va statistik jarayon nazorati gʻoyalari asosida past sifat xarajatini nuqson ulushi, aniqlash darajasi va yolgʻon signal ulushi orqali ifodalovchi model taklif etildi. Statistika agentligi maʼlumotlariga koʻra, kichik tadbirkorlikning sanoat mahsulotidagi ulushi 2025-yilda 34,9 foizni tashkil etgani qayd etildi. Shartli korxona misolida sunʼiy intellekt vizual nazorati past sifat xarajatini 64,9 foizga qisqartirgani, investitsiya 3,1 yilda qoplanishi va zararsizlik chegarasi 2,8 foizli nuqson ulushiga toʻgʻri kelishi hisoblandi. Muallif natijani aniqlash darajasidan koʻra koʻproq tashqi nosozlik qiymati belgilashini asosladi va kichik korxonalar uchun sifat darvozasi modelini ishlab chiqdi.

References

1. Huggins, L. P. (1998). Total quality management and the contributions of A.V. Feigenbaum. Journal of Management History, 4(1), 60–67. https://doi.org/10.1108/13552529810369623

2. Schiffauerova, A., & Thomson, V. (2006). A review of research on cost of quality models and best practices. International Journal of Quality & Reliability Management, 23(6), 647–669. https://doi.org/10.1108/02656710610672470

3. Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. John Wiley & Sons. https://doi.org/10.1002/9780470258354

4. Linderman, K., Schroeder, R. G., Zaheer, S., & Choo, A. S. (2003). Six Sigma: A goal-theoretic perspective. Journal of Operations Management, 21(2), 193–203. https://doi.org/10.1016/S0272-6963(02)00087-6

5. Ittner, C. D. (1996). Exploratory evidence on the behavior of quality costs. Operations Research, 44(1), 114–130. https://doi.org/10.1287/opre.44.1.114

6. Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386. https://doi.org/10.1080/00224065.1999.11979944

7. Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications: A survey. Sensors, 20(5), 1459. https://doi.org/10.3390/s20051459

8. Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.01.003

9. Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/CVPR.2019.00982

10. Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., & Gehler, P. (2022). Towards total recall in industrial anomaly detection. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 14298–14308). IEEE. https://doi.org/10.1109/CVPR52688.2022.01392

11. Tercan, H., & Meisen, T. (2022). Machine learning and deep learning based predictive quality in manufacturing: A systematic review. Journal of Intelligent Manufacturing, 33(7), 1879–1905. https://doi.org/10.1007/s10845-022-01963-8

12. Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), 1118–1136. https://doi.org/10.1080/00207543.2017.1372647

13. Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005

14. Oʻzbekiston Respublikasi Prezidenti huzuridagi Statistika agentligi. (2026). Kichik tadbirkorlik subyektlarining jami ishlab chiqarilgan sanoat mahsulotlari (xizmatlari)dagi ulushi (1.10.02.0007-koʻrsatkich) [SIAT maʼlumotlari]. https://api.siat.stat.uz/media/uploads/sdmx/sdmx_data_374.pdf

15. Oʻzbekiston Respublikasi Prezidenti huzuridagi Statistika agentligi. (2026). Oʻzbekiston Respublikasining ijtimoiy-iqtisodiy holati: 2025-yil yanvar-dekabr. https://stat.uz/img/press-relizlar/2_-talil-2025-j_-yanvar-dekabr-lotin-_p58591.pdf

16. Oʻzbekiston Respublikasi Prezidentining 2024-yil 14-oktabrdagi PQ-358-son “Sunʼiy intellekt texnologiyalarini 2030-yilga qadar rivojlantirish strategiyasini tasdiqlash toʻgʻrisida”gi Qarori. https://lex.uz/docs/-7158604

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Published

2026-10-20

How to Cite

KICHIK BIZNES SUBYEKTLARIDA SUNʼIY INTELLEKT ASOSIDA ISHLAB CHIQARISHDAGI NUQSONLARNI KAMAYTIRISHNING IQTISODIY SAMARADORLIGI. (2026). Scientific Journal of Actuarial Finance and Accounting, 6(10), 138-147. https://doi.org/10.55439/AFA/vol6_iss10/1540