The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

Subject Areas : Mathematical Optimization

Farzad Tahriri ¹ , Maryam Mousavi ² , Siamak Hozhabri Haghighi ³ , Siti Zawiah Md Dawal ⁴

1 - Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
2 - Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
3 - Department of Mechanical Engineering, Shiraz University, 71936, Shiraz, Iran
4 - Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

Received: 2020-10-02 Accepted : 2020-10-02 Published : 2014-09-01

Keywords: Output, Supplier selection . Fuzzy Delphi method .Fuzzy inference system . Multiple, inputs multiple,

Abstract :

In today’s highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers’ ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker’s assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including ‘‘extremely preferred’’, ‘‘moderately preferred’’, and ‘‘weakly preferred’’. In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.