Improving consistency classification: An innovative benchmark‐based approach for the <scp>AHP</scp>

Abstract The analytic hierarchy process (AHP) is a cornerstone of multi‐criteria decision analysis, enabling well‐informed choices across diverse contexts. This paper introduces an original benchmark‐based framework designed to enhance the precision consistency classification for pairwise comparison matrices (PCMs) within AHP methodology. innovative approach quantifies discrepancy between given PCM and its benchmark matrix, comprising ratios that faithfully reflect relative preferences encapsulated principal eigenvector values, thereby capturing true degree coherence. To ensure alignment with human perception, elements are further rounded nearest values on Fundamental Scale. potency our derives from two pivotal factors: inherent Priority Preference Range order PCM. Statistical thresholds established using technique based simulated, logical PCMs, proposed by Bose [2022]. rigorous method ensures unbiased, objective pragmatic evaluation consistency, eliminating subjectivity in arbitrary random PCMs. Our rectifies inconsistencies conventional CR yields false positives PCMs orders 3 4, negatives higher orders. By harnessing customized benchmarks eschewing matrices, systematically confronts challenges AHP, thus enhancing decision‐making capability. practical utility aptly demonstrated through AHPtools, R‐based library package showcase novel method. demonstration Appendix B will facilitate readers easily apply methodology real‐world scenarios AHP. In conclusion, heralds transformative era empowering unprecedented reliability, ushering new paradigm informed astute outcomes.