Artificial Intelligence-Enabled Supply Chain Risk Management and Its Impact on Project Cost Efficiency: An Empirical Investigation
Abstract
Artificial intelligence (AI) is increasingly transforming supply chain risk management by improving the identification, prediction, and management of operational uncertainties. These capabilities may also contribute to better project cost performance by supporting timely and informed decision-making. This study aimed to assess the application of AI-enabled supply chain risk management (AI-SCRM), examine its relationship with project cost efficiency, and determine its predictive effect on project cost outcomes. A quantitative cross-sectional design was employed using a structured questionnaire administered to 80 professionals working in supply chain management, project management, procurement, logistics, and operations. Data were analysed using Microsoft Excel. Descriptive statistics were used to summarize response patterns, while independent-samples t-test, Chi-square analysis, Pearson correlation, and linear regression were applied for inferential analysis. The findings showed favourable perceptions of AI applications in risk identification, disruption forecasting, supplier monitoring, demand and inventory forecasting, and decision support. A significant association was observed between AI-SCRM and project cost efficiency. Correlation analysis further demonstrated a significant positive relationship between the two constructs, while regression analysis identified AI-SCRM as a significant positive predictor of project cost efficiency. However, project cost-efficiency perceptions did not differ significantly by gender. The findings demonstrate that stronger AI-enabled supply chain risk-management capabilities are associated with improved project cost efficiency. Effective AI integration can support cost control, procurement efficiency, resource utilization, and responses to supply chain uncertainties, highlighting its practical value for project and supply chain management.