Ключевые слова
Аннотация
Mazkur maqolada kichik biznes subyektlarining omborxona faoliyatini samarali tashkil etish va boshqarish uchun past xarajatli Data Science yechimlarining qo‘llanilishi yoritiladi. Omborxona resurslarini optimallashtirish, real vaqtda inventar nazorati, talab prognozi va avtomatlashtirilgan hisob-kitoblar orqali mahsulot aylanishini kuchaytirish mumkinligi ko‘rsatib o‘tiladi. Arzon va ochiq manba (open-source) texnologiyalar asosida ishlab chiqilgan vositalar — jumladan Python, Google Sheets, Power BI, va boshqa ilovalar misolida real yechimlar tavsiya etiladi. Maqola kichik korxonalar egalariga, logistika xodimlariga va Data Science bilan shug‘ullanuvchi amaliyotchilarga mo‘ljallangan.
Библиографические ссылки
Sharma, M., & Patel, K. (2020). Low-Cost Data Analytics for Small Business Inventory Optimization. Journal of Small Business Technology, 8(2), 113–129.
Rasel, T. (2019). Smart Warehousing for SMEs: A Practical Approach to Data-Driven Inventory Control. Logistics & Technology Review, 6(3), 87–101.
OECD. (2021). Digital Transformation of SMEs: Challenges and Policy Considerations. OECD Digital Economy Papers, No. 314. Paris: OECD Publishing. https://doi.org/10.1787/31c5b49e-en
World Bank. (2020). Enabling the Digital Economy for Small and Medium Enterprises. Washington, DC: World Bank Group.
Floridi, L. (2018). Soft Ethics and the Governance of the Digital. Philosophy & Technology, 31(1), 1–8.
Zokirov, A., & Abdullaeva, N. (2022). Raqamli yechimlar yordamida logistika jarayonlarini optimallashtirish. TATU Ilmiy-texnik jurnali, 4(2), 55–68.
GitHub. (2023). Inventory Management Tools for SMEs. https://github.com/inventory-tools-sme
Power BI Documentation – Microsoft Docs. (2022). Create Inventory Dashboards. https://learn.microsoft.com/en-us/power-bi/
Google Developers. (2021). Google Sheets API for Inventory Automation. https://developers.google.com/sheets/api
Pandas Documentation. (2023). Data Analysis Library in Python. https://pandas.pydata.org/docs/