A FRAMEWORK-BASED META-ANALYSIS OF ARTIFICIAL INTELLIGENCE-DRIVEN ERP SOLUTIONS FOR CIRCULAR AND SUSTAINABLE SUPPLY CHAINS

Abstract

This study conducted a comprehensive framework-based meta-analysis to evaluate the impact of Artificial Intelligence–enabled Enterprise Resource Planning (AI-ERP) systems on circular and sustainable supply chain performance, drawing evidence from 270 empirical studies published across multiple regions and industrial sectors. A structured screening protocol identified quantitative findings related to waste reduction, material recovery, recycling efficiency, energy savings, emission performance, and closed-loop coordination. Descriptive results showed that manufacturing industries accounted for over 50% of included studies, with Asia and Europe contributing 70% of the total evidence base. Correlation analysis demonstrated consistently positive associations between AI-ERP capabilities and sustainability outcomes, with optimization analytics showing the strongest correlation with energy savings (r = 0.58), traceability modules showing a high correlation with recycling efficiency (r = 0.62), and automated decision engines demonstrating the highest association with closed-loop performance (r = 0.57). Meta-regression findings revealed statistically significant positive effects across all AI-ERP constructs, with optimization analytics producing the largest predictive coefficient (β = 0.27) and AI forecasting contributing meaningfully to waste reduction (β = 0.18). Moderator analysis indicated that digital maturity amplified effect sizes by an average of 21%, while regulatory intensity increased predictive strength by 17%. Collinearity diagnostics confirmed the robustness of the regression models, with all variance inflation factors remaining below 2.7. Validity assessments showed high coding reliability (Cohen’s kappa = 0.87) and strong methodological quality across studies. Overall, the findings demonstrated that integrating AI-enabled ERP systems significantly enhances circularity outcomes by improving prediction accuracy, optimizing resource use, strengthening traceability, and accelerating closed-loop operations. The study provides quantifiable evidence that AI-ERP architectures represent a critical enabler of resource-efficient, data-driven, and sustainability-aligned supply chain transformation across global industrial contexts.