In complex decision-making environments involving multiple conflicting criteria, the need for robust and insightful evaluation methods is increasingly critical. This study aims to address the inconsistencies among Multi-Criteria Decision Analysis (MCDA) methods, which often yield divergent rankings for the same problem. To overcome this challenge, we propose a novel Compromise Fuzzy Ranking (CFR) method that integrates both positional rankings and preference scores, offering a more balanced and informed consensus in decision-making. The CFR method is evaluated through theoretical analysis and simulation studies, demonstrating its ability to produce more consistent and interpretable results compared to traditional compromise approaches. The key benefit of CFR lies in its capacity to capture the complementary strengths of different ranking perspectives, thereby enhancing the quality, transparency, and reliability of decision support systems.