Measuring the success possibility of implementing advanced manufacturing technology by utilizing the consistent fuzzy preference relations

Yusuff et al. [Yusuff, R. M., Yee, K. P., & Hashmi, M. S. J. (2001). A preliminary study on the potential use of the analytical hierarchical process (AHP) to predict advanced manufacturing technology (AMT) implementation. Robotics and Computer Integrated Manufacturing, 17, 421–427.] presented the analytic hierarchy process (AHP) for forecasting the success of advanced manufacturing technology (AMT) implementation based on seven influential factors, including a committed and informed executive sponsor, an operating sponsor, think-tank linkage, alignment of business, integration with the existing system, natural organizational interface to the system, user commitment and support (Yusuff et al, 2001). Owing to the fact that AHP method performs complicated pairwise comparison among elements (attributes or alternatives), and it takes considerable time to obtain a convincing consistency index with an increasing number of attributes or alternatives. This study therefore applies the consistent fuzzy preference relations (CFPR) [Herrera–Viedma, E., Herrera, F., Chiclana, F., & Luque, M. (2004). Some issues on consistency of fuzzy preference relations. European Journal of Operational Research, 154, 98–109.] to tackle the aforementioned shortcomings of Yusuffs’ et al. work. The analyzed prediction outcomes obtained by CFPR almost coincide with that ones produced by AHP approach. Notably, the ratio of the pairwise comparison times of the priority weights for the seven influential factors between CFPR and AHP is 6:21, because CFPR uses simple reciprocal additive transitivity from a set of n − 1 preference data, rather than reciprocal multiplicative transitivity from a set of n(n-1)2 preference values, an approach that facilitates the computation procedures as well as boosts the effectiveness of implementing the AMT decision problems. Namely, CFPR takes the least (n − 1) judgments in pairwise comparison, whereas the AHP uses n(n-1)2 judgments in paired comparison to establish a preference relation matrix with n elements. Besides, the comparative results not only show that consistent fuzzy preference relations is computationally more efficient than analytic hierarchy process, but also demonstrate its applicability and feasibility in dealing with complicated hierarchical multi-attribute decision-making problems.