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AI-DRIVEN DEMAND FORECASTING AS A TOOL FOR SUSTAINABLE FMCG DISTRIBUTION

Affiliation
Toshkent Davlat Iqtisodiyot Universiteti image/svg+xml

Annotatsiya

This thesis explores AI-driven demand forecasting as a strategic tool for sustainable FMCG distribution. It shows how AI enhances inventory accuracy, reduces waste, and optimizes logistics. Global best practices (Unilever, P&G) illustrate realworld applications, while the case for Uzbekistan emphasizes digital integration to align with green economy goals.

References

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  2. Chopra, S., & Meindl, P. (2021). Supply Chain Management: Strategy, Planning, and Operation. Pearson Education
  3. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021). Global Supply Chain and Operations Management. Springer
  4. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84
  5. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889
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  8. Ghosh, A. (2022). AI and sustainability: The future of green supply chains. Journal of Cleaner Production, 356, 131805
  9. Unilever. (2022). Sustainability Progress Report. Unilever Global
  10. Asian Development Bank. (2021). Green Economy Transition in Central Asia. ADB Publications
  11. Procter & Gamble. (2023). Annual Report 2023. P&G
  12. Deloitte. (2020). AI-powered supply chains: Smart forecasting for a better future. Deloitte Insights
  13. McKinsey & Company. (2019). Smart operations: The rise of AI in emerging markets. McKinsey Global Institute
  14. World Bank. (2022). Digital Transformation of Supply Chains in Developing Countries. World Bank Reports
  15. OECD. (2021). Sustainable Development and Green Growth in Emerging Economies. OECD Publishing

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