A Decision-Making Framework for Artificial Intelligence Implementation Strategies in Circular Supply Chains

The growing complexity of global supply chains has intensified the need for sustainable and circular approaches that integrate technological innovation, environmental responsibility, and social resilience. This study develops a multi-criteria decision-making framework to evaluate and rank alternative artificial intelligence (AI) implementation strategies for circular supply chains (CSC), considering benefits, opportunities, and costs, followed by a risk-based assessment. The framework applies the best-worst method (BWM) and the analytic hierarchy process (AHP) to support the strategic evaluation of AI-related alternatives, incorporating sub-criteria to enable a more granular assessment. Methodologically, the study advances existing decision-support approaches by integrating BWM and AHP within a unified multi-criteria framework that jointly evaluates benefits, opportunities, and costs and is further extended through a multidimensional risk-based assessment. The integration of BWM and AHP within a unified framework enables a transparent ranking of AI strategies and extends the analysis through a multidimensional risk-based assessment. Results indicate that benefits, particularly improved data-driven decision accuracy, dominate the evaluation, while hybrid and centralized AI strategies emerge as the most effective options once risks are incorporated. This provides a comprehensive and practical perspective for managers seeking to align AI adoption with CSC objectives