Miraç Eren

Determination of Extended Fuzzy TOPSIS Method of Criteria Leading to Supplier Selection for Industries

Supplier selection decisions require the choices of individual purchases to determine products and order quantities from certain suppliers. When suppliers are evaluated according to well-defined criteria, their level of effort and attention to detail is expected to increase significantly. This can provide to improve productivity. So, determination of supplier selection criteria that is a multi-criteria decision making process can include both quantitative and qualitative elements that are very important. In this article, the aim is to offer a fuzzy decision making method to determine leading criteria, in terms of supplier selection for an automobile company that operates in Iran, because the right suppliers that are selected according to well-defined criteria can significantly reduce the material purchasing costs and improve corporate competitiveness. For that purpose, linguistic values are used for expressing fuzzy numbers in order to assess the ratings and weights of these factors by considering criteria such as the decisions made by people who work in the purchasing department of the company. Afterward, the fuzzy TOPSIS method that is a multiple criteria decision making (MCDM) method according to fuzzy set theory is presented to cope with the determination of supplier selection criteria process. According to the results, it is determined that the following are important for supplier selection criteria for automobile firms: delivery, the amount of past business, reciprocal arrangements, warranties, and geographical location.

Application of Panel Quantile Regression and Gravity Models in Exploring the Determinants of Turkish Automotive Export Industry

This paper purposes to determine potential factors influencing the amount of Turkish automotive industry exports. For this purpose, the available data of 68 major trading partners of Turkey in terms of automotive industry exports were utilized for the sample period 2007–2015. Both panel quantile regression and the gravity model of trade approaches were considered to analyze the relevant data. The empirical findings of this paper revealed that the population and the distance variables were found as statistically significant for all quantiles, while the former has positive and the latter has negative signs as expected. Results also indicated that there was a statistically significant positive correlation between GDP per capita and the amount of Turkish automotive industry exports at 10 and 50% quantiles; however, it was not statistically significant at 90% quantile despite its positive sign. Among Turkey’s exporter countries, being a EU member country dummy variable was found to have a statistically significant positive impact on the amount of automotive industry exports. Real exchange rate was not found as a significant determinant of the amount of automotive industry exports. In the lights of empirical evidence obtained from this study, several recommendations were made for Turkey’s future international trade policies.

Application of linear programming to derive the local weight in the analytic hierarchy process

The analytic hierarchy process (AHP) has become more developed in both the areas of theory and practice. The important topic here, is how to drive the local weight vector from a pairwise reciprocal matrix. In the literature, there are several methods used to accomplish this. Most recently, Hosseinian et al. (2009) suggested the LP-GW-AHP because it obviously provides better weights. In this article, the LP-GW-AHP method is applied to multi-level decision problems, and the weights were compared with Saatys eigenvector. According to our findings, of the LP-GW-AHP method, Saatys eigenvector method differs slightly from that derived in the local weight values.