OPTIMIZATION OF BUSINESS PROCESSES, SUPPLY CHAINS AND OPERATIONAL EFFICIENCY THROUGH INTELLIGENT SYSTEMS AND PREDICTIVE ANALYTICS

Abstract

This research examines the transformative impact of artificial intelligence (AI) and big data analytics on corporate operational efficiency, supply chain resilience, and business process optimization. Based on comprehensive analysis of empirical data from McKinsey Global Surveys (2024-2025), and the UNDP's "Digital Economy of Uzbekistan" (2025) study, and contributions from Uzbek scholars including Zukhurova N.A. (TUIT), Yuldashev A.A. (TSUE), the study demonstrates that organizations effectively integrating AI-driven predictive analytics achieve 20-30% reduction in inventory costs, up to 50% improvement in forecasting accuracy, and 15-25% enhancement in overall operational efficiency. With 88% of global organizations now reporting regular AI use and 65% adopting generative AI, the paradigm has shifted from experimentation to strategic scaling. The study specifically analyzes the context of Uzbekistan, where the Government AI Readiness Index ranking has significantly improved from 87th (2023) to 62nd (2025) place. Through detailed comparative case studies of Siemens, Microsoft, Amazon, DHL, and Alibaba (Cainiao), the research synthesizes global best practices for emerging economies. Incorporating insights from Uzbek scholars including Zukhrova N.A. (TUIT) and Yuldashev A.A. (TSUE), the paper proposes a strategic roadmap for 2026–2030, targeting a $10 billion contribution to Uzbekistan's GDP by 2030.