Application of Artificial Intelligence in Supply Chain Management: Empirical Analysis of Optimization and Efficiency Enhancement

Abstract

In an era of increasing global uncertainty and rapidly evolving market dynamics, supply chains have become more vulnerable to disruptions caused by pandemics, geopolitical conflicts, natural disasters, and demand variations. The analysis is based on surveyed firms, which limit applicability across all industries and regions. Furthermore, reliance on self-reported data could affect measurement accuracy. The framework examines how artificial intelligence (AI) is used in supply chain management (SCM), with an importance on efficacy improvement and optimization through empirical analysis. Data were gathered through surveys from 534 firms, and exploratory analyses were conducted to validate the construct structures. The relations between the variables were investigated using correlation analysis, and the proposed processes and mediation effects were tested using partial least squares structural equation modeling (PLS-SEM). Reliability and validity tests were performed to guarantee measurement accuracy, providing robust empirical insights into AI-driven supply chain performance. The approach examines how a company's propensity to use AI is impacted by supply chain cooperation, environmental uncertainty, and the relative benefits of AI technology. It further assesses how AI adoption influences supply chain optimization, effectiveness enhancement, resilience, and overall performance. EU shows a high correlation with ASU (0.74), suggesting that improved ease of use strongly supports usability, underscoring its central role in supply chain performance. Moreover, AI adoption positively impacts optimization and efficiency through improved forecasting, inventory control, and logistics coordination, while also enhancing supply chain resilience, enabling firms to respond more effectively to external disruptions. The framework connections the gap between AI adoption theory and practical supply chain developments, presenting actionable insights for enterprises seeking to integrate AI into their supply chain strategies to achieve sustainable efficiency and performance gains.