Application of combined MADM methods as MOORA and PSI for ranking of FMS performance factors

Abstract

Purpose Due to the increasing demand of customer and competitive market pressure, manufacturing organizations should be modernized in strategies, production operations, processes and its procedures to remain competitive. So, a flexible manufacturing system (FMS) was adopted by the manufacturing system to fight with competitive pressure. The purpose of this paper is to enhance the performance of manufacturing system, with a focus on its factors. Design/methodology/approach In this research, the ranking of the performance factor of FMSs is done by using multiple attribute decision-making (MADM) methods as multi-objective optimization on the basis of ratio analysis (MOORA) and preference selection index (PSI). Weights of attributes are defined by the AHP method. Findings Ranking of performance factor is done on the basis of six variables which affect three elements of performance of FMS, i.e. productivity, flexibility and quality. MOORA is applied in three ways such as the ratio-based, reference point and full multiplicative method. In the MOORA method, ranking was done considering weights of attributes and also without it. A PSI method is used to find the best factor among the factors. The results of these methodologies, i.e. MOORA and PSI, are same, i.e. productivity is the primary factor in the manufacturing system. The ranking is validated by the result of different methodology used in this research. Practical implications This research has evaluated the important factors and performance variables which can enhance the performance of manufacturing organizations. So, the manufacturing persons can focus on these to enhance its performance. Originality/value Combined MADM methods, i.e. MOORA and PSI methodologies, are used in this paper to deal with the ranking of performance factors of the FMS considering qualitative characteristics. These approaches show the conversion of a qualitative attribute to quantitative attributes by using fuzzy logic.