MACHINE MAINTENANCE MANAGER SELECTION PROCESS WITH FUZZY TOPSIS TECHNIQUE: AN EMPIRICAL APPLICATION

Abstract

Because of the fact that today human resources has been accepted as one of the most important source of competitive advantage of an organization, finding the right person for the job has become as a vital human resource management function. In this context, determining the approach to be used in the selection process is prerequisite. As a result of that the decision makers use linguistic variables while evaluating multiple criteria and candidates, human resource selection process based on the qualitative more than quantitative data brings vagueness and fuzziness. This paper presents fuzzy TOPSIS method being used while group decision making in the fuzzy environment and displays the method’s process with an application. For this purpose, as decision makers, three top managers in a business organization that is in the list of “First 500 Big Industrial Organizations of Turkey” evaluated decision criteria and the candidates by using linguistic variables for the positions of machine maintenance manager. These verbal data were transformed into triangular fuzzy numbers for fuzzy TOPSIS method. According to fuzzy TOPSIS, the candidates were ranked from the best to the worst with respect to the calculated closeness coefficients. This study shows that for deciding more accurately and effectively in the human resource selection process, fuzzy TOPSIS model is considerably suitable as an approach of fuzzy multi-criteria decision making.