انتخاب تامین کننده در بازار الکترونیک با استفاده از جلب رضایت و تحلیل سلسله مراتبی فازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19205||2010||9 صفحه PDF||سفارش دهید||7680 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 1, January 2010, Pages 490–498
Supplier selection is a critical and demanding task for companies that participate in electronic marketplaces to find suppliers and to execute electronically their transactions. This paper is aimed to suggest a fresh approach for decision support enabling effective supplier selection processes in electronic marketplaces. We introduce an evaluation method with two stages: initial screening of the suppliers through the enforcement of hard constraints on the selection criteria and final supplier evaluation through the application of a modified variant of the Fuzzy Preference Programming (FPP) method. The proposed method alleviates the information overload effect that is inherent in the environment of electronic marketplaces, facilitates an easier elicitation of user preferences through the reduction of necessary user input (i.e. pairwise comparisons) and reduces computational complexity, in terms of the number of linear programs to be solved, in comparison with the original FPP method. The FPP method is adopted and modified accordingly in order to tackle the issue of inconsistency/uncertainty of human preference models. Our approach is demonstrated with the example of a hypothetical metal manufacturing company that finds and selects suppliers in the environment of an electronic marketplace.
Business-to-Business commercial transactions are increasingly executed electronically. As indicated by the Economic Review of the Federal Reserve Bank of Kansas City, between 1999 and 2002, B2B e-commerce sales grew at an annual rate of 5.5 percent in the United States (Willis, 2004). Moreover, since the end of the nineties electronic marketplaces have played the role of an aggregator that merges potentially thousands of suppliers and customers as B2B electronic hubs. In the context of such environments, the critical task of supplier selection becomes an arduous procedure since the decision maker must make a choice between an abundance of alternatives. Furthermore, decision makers’ interest about the supplier selection process has been continuously growing because reliable suppliers enable the reduction of inventory costs and the improvement of product quality (Braglia & Petroni, 2000). There is an agreement in relevant literature (Choy, Lee, & Lo, 2002) that selecting appropriate suppliers is a complicated issue because of the large number of criteria to be considered as well as because criteria are both quantitative (e.g. price, distance, delivery time) and qualitative (e.g. quality, design/technological capability, finances). Thus, there is an ongoing effort in the research community to develop appropriate techniques to grasp preferences and to define evaluation models and algorithms (Yang & Chen, 2006). Moreover, an electronic marketplace environment entails a potentially large number of candidate suppliers as well as the participation of buyer organizations with divergent needs and characteristics, which in their turn correspond to the identification of different decision criteria for the selection of suppliers (Pearson & Ellram, 1995). Therefore, electronic marketplaces make an effort to provide effective decision support services for supplier assessment and selection to their participants in order to enhance their satisfaction and loyalty (e.g. Ariba, Emptoris, Perfect Commerce, etc.) (Bartels, 2005). The present paper, proposes a fresh approach for the provision of decision support to solve the supplier selection problem in an electronic marketplace environment. In particular, we propose a decision method that combines a special satisficing technique to prune the supplier selection search space with the application of a supplier ranking technique, a modified variant of the Fuzzy Preference Programming Method (Mikhailov, 2000), for the final supplier evaluation. A contribution of our work is that we modify Mikhailov’s FPP method according to Liberatore’s rating scale AHP method (Liberatore, 1987). This combination has both the advantages of the usual rating scale AHP approach and of the FPP method: it overcomes the explosion in the number of pairwise comparisons when the number of alternatives and/or the number of criteria is large (rating scale AHP) and at the same time solves the problem of inconsistency and of uncertain human preference models by using interval values for preference relations (FPP). The effectiveness of our approach is demonstrated through the provision of a numerical example of a metal manufacturing company that finds and selects suppliers in the environment of an electronic marketplace providing decision support services on this basis. The remainder of this paper is organized as follows. Section 2 describes a background of the supplier selection process and related work. Section 3 introduces the proposed approach for supplier selection, consisting of supplier pre-qualification using Simon’s satisficing model and supplier raking using a fuzzy AHP method. In Section 4 the proposed method is illustrated by a numerical example. Section 5 wraps up the paper with some concluding remarks.
نتیجه گیری انگلیسی
Activities related to obtaining products and materials from outside suppliers are increasingly executed in electronic marketplace environments. Thus, concerns about the effectiveness and rationality of the critical task of supplier selection have become central for the decision makers of buyer organizations. In this context, a number of issues arise in a decision environment characterized by uncertainty: efficient and easy capturing of buyer preferences, efficient management and processing of an abundance of information, efficient and rational evaluation of competing suppliers. The aim of this paper has been to propose a fresh approach for the provision of decision support addressing these problems. On the one hand we propose the use of a satisficing technique to prune the supplier selection search space for supplier pre-qualification, on the other hand we introduce a modified, rating-scale version of the FPP method for final supplier evaluation. Our approach alleviates the information overload effect that is inherent in the environment of electronic marketplaces, facilitates an easier elicitation of user preferences through the reduction of necessary user input (i.e. pairwise comparisons) and reduces computational complexity, in terms of the number of linear programs to be solved, in comparison with the original FPP method. At the same time, our approach handles inconsistency and uncertainty of the preference models of the decision makers by adopting and modifying the FPP method. Our work can be extended in the direction of integrating the proposed supplier selection method in the decision support infrastructure of an electronic marketplace in order to evaluate it in a real environment.