Fuzzy inference to supplier evaluation and selection based on quality index: a flexible approach
Supplier evaluation and selection are very important issues in todays highly competitive global business environment. Many studies have inferred that high quality has a positive, direct impact upon increasing profitability, through lowering operating costs and improving market share; therefore, quality is regarded as the most important factor for supplier evaluation and selection among various criteria. Among various quality control and assurance activities, process capability index C pk is the most widely used index in making managerial decisions as it provides bounds on the process yield for normally distributed processes. However, existing methods for assessing process performance, which were constructed by statistical inference may unfortunately lead to fine results, because uncertainties exist in most real-world applications. Thus, this study adopts fuzzy inference to deal with testing of C pk. A brief score is obtained for assessing a suppliers process instead of a severe evaluation. Moreover, this study extends the proposed approach to conduct the supplier selection problem, which can significantly avoid ambiguous results. Finally, a real example is examined to illustrate the applicability of the proposed approach.
supplier evaluation quality control supplier selection fuzzy sets process capability analysis