Fuzzy Analytic Hierarchy Process for Determination of Supply Chain Performance Evaluation Criteria
Fuzzy AHP (Analytic Hierarchy Process) method is decision-making way at the end of integrating the current AHP method with fuzzy structure. In this study, the processes of production planning, inventory management and purchasing department of a system were analysed and were requested to decide the performance criteria of each area. At this point, the current work processes were analysed by various decision-makers and comparing each criteria by giving points according to 1-9 scale were completed. The criteria were listed in order to their weights by using Fuzzy AHP approach and top three performance criteria of each department were determined. After that, the performance criteria of supply chain consisting of three departments were asked to determine. The processes of each department were compared by decision-makers at the point of building the supply chain performance system and getting the performance criteria. According to the results, the criteria of performance system of supply chain by using Fuzzy AHP were determined for which will be used in the supply chain performance system in the future.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316059Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 738
 Demir, H. I., Uygun, O., Cil, I., Ipek, M., and Sari, M. Process planning and scheduling with SLK due-date assignment where earliness, tardiness and due-dates are punished. Journal of Industrial and Intelligent Information Vol, 3(3), 2015.
 Cil, I. and Evren, R., Linking of manufacturing strategy, market requirements and manufacturing attributes in technology choice: an expert system approach. The engineering economist, 43(3), 1998, 183-202.
 Cil, I., Erdil, N. O., Kılıc, T., & Kosar, B.December). Lean logistic network design and analysis with anylogic. XIV. International logistics and supply chain congress (p. 523), 2016.
 Sun, CC, A Performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods, Expert Systems with Applications 37- 2010, 7745-7754
 Cil, I, Turkan, YS, An ANP-based assessment model for lean enterprise transformation, The International Journal of Advanced Manufacturing Technology, Int J Adv Manuf Technol, 64, 2013,1113-1130,
 Hosseini Nasab, Hassan, and Mona Mirghani Ghamsarian, A fuzzy multiple-criteria decision-making model for contractor prequalification, Journal of Decision Systems, 24, 4, 2015, 433-448.
 Cil, I., and T. Cakar. Using Web based influence allocation processes based on experts' opinion immediately after natural catastrophe, International Journal of Industrial Engineering-Theory Applications and Practice 12.4, 2005, 407-418.
 Sun, Chia-Chi, Grace TR Lin, and Gwo-Hshiung Tzeng, The evaluation of cluster policy by fuzzy MCDM: Empirical evidence from HsinChu Science Park, Expert Systems with Applications,36.9,2009,11895-11906.
 Gharakhani, D., and Madanshekaf, S, Prioritization of Effective factors on customers' satisfaction in the sector of banking services (Case Study: Refah bank of ZanjanProvince), 2009.
 Rafiuzzaman, M. and Çil, I., A Fuzzy Logic based Agricultural Decision Support System for Assessment of Crop Yield Potential using Shallow Ground Water Table. International Journal of Computer Applications 149.9, 2016.
 Sun, Chia-Chi. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications 37.12 2010, 7745-7754.