Fuzzy multi criteria decision making approach in performance evaluation: The case of Turkish manufacturing enterprises

Against continuous development of economic and technologic advances, enterprises are in the position of making strategic decisions to maintain their life and compete. A good implementation of performance evaluation influences widely the success of firms' prospective decisions. In this respect, performance evaluation concept both plays a role in efficient use of resources and guides managers about detection of the competition status in industry. Due to the importance of performance evaluation and its structure which requires consideration of multiple factors, in this study it is benefited from "Fuzzy Multiple Criteria Decision Making Approach" instead of conventional methods used in this subject before. In application, it is purposed to make a performance evaluation of the enterprises which operate in Turkish Manufacturing Industry by using Fuzzy Analytic Hierarchy Process (AHP), TOPSIS, VIKOR and Grey Relational Analysis (GRA) methods. Within this scope in the light of determined performance evaluation criteria, criteria are weighted with Fuzzy AHP by receiving opinions of experts. Afterwards, for the aim of obtaining performance rankings of 171 firms regarding 8 sectors that take part in Turkish Manufacturing Industry by year of 2015, it is used TOPSIS, VIKOR and GRA techniques. For reaching a single performance ranking outcome, it is profited by Borda Count technique by integrating the three mentioned rankings acquired from each three methods. As a result of application, it is revealed performance rankings of manufacturing enterprises with proposed fuzzy decision making method and provided a general performance evaluation. The model is believed to be helpful for managers as a practical and efficient decision making tool about performance evaluation issue and make contribution to manufacturing industry. Key Words: Fuzzy Decision Making, Fuzzy AHP, TOPSIS, VIKOR, Grey Relational Analysis, Performance Evaluation