THE IMPACT OF ARTIFICIAL INTELLIGENCE ADOPTION IN SUPPLY CHAIN MANAGEMENT ON ORGANIZATIONAL PERFORMANCE: EVIDENCE FROM UZBEKISTAN

Jahongir Nabiev

Authors

Jahongir Nabiev Author

Abstract

This study investigates the empirical relationship between the adoption of Artificial Intelligence (AI) in supply chain management (SCM) and firm-level organizational performance in Uzbekistan's evolving economic landscape. As a doubly landlocked nation navigating the structural shifts of the "Digital Uzbekistan 2030" strategy, the country’s transport and distribution sectors face unique friction points, including volatile border-clearance windows, land-transit dependencies, and underutilized fleet capacity. Drawing on a mixed-methods approach utilizing survey data from logistics managers, safety specialists, and operational heads across Tashkent and regional hubs, this paper analyzes how predictive analytics, dynamic routing, and automated safety compliance systems impact operational efficiency and financial outcomes. The findings indicate that while AI-driven demand forecasting and predictive fleet maintenance significantly reduce transit lead-time variance and vehicle downtime, the full realization of organizational performance is heavily moderated by legacy data silos and localized capital constraints. This research bridges a critical gap in international business and management literature by evaluating AI implementation dynamics within a transitioning Central Asian trade corridor.

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