Robust Decision-Making for Digital Supply Chain Transformation Using a q-Rung Orthopair Fuzzy OrdPA-MARCOS Framework

This paper proposes a hybrid decision-making framework for assessing the Digital Supply Chain (DSC) in uncertain situations using the Ordinal Priority Approach (OrdPA) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model in a q-Rung Orthopair Fuzzy Set (q-ROFS) context. The OrdPA-q-ROF-MARCOS model utilizes \textit{q-ROFS} to adjust expert hesitancy, weight criteria using ordinal preferences, and rank alternatives using compromise solutions. The framework's robustness is shown by its consistent ranking of five essential DSC elements with different q values ($q=1,2,3,4,5$) in sensitivity analysis.The proposed model proves its superiority and validity by (1) demonstrating perfect rank stability across q-parameter variations, Dombi parameter adjustments, and criteria weight perturbations, and (2) achieving identical ranking convergence compared to q-ROF-TOPSIS, q-ROF-VIKOR, and q-ROF-COPRAS. The proposed framework eliminates rank reversal, lowers parameter sensitivity, and provides e.g. The results show that blockchain is the most essential aspect, followed by data integration, cybersecurity, IoT implementation, and AI/analytics. The work advances fuzzy decision-making theory and provides realistic supply chain digital transformation planning help.