روش TOPSIS فازی یکپارچه و روش MCGP برای انتخاب تامین کنندگان در مدیریت زنجیره تامین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19304||2011||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 9, September 2011, Pages 10803–10811
Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.
Supplier selection is an important issue in supply chain management. Typically, manufacturer spends more than 60% of its total sales on purchased items, such as raw materials, parts, and components (Krajewsld & Ritzman, 1996). In addition, manufacturer purchases of goods and services constitute up to 70% of product cost (Ghodsypour & O’Brien, 1998). Therefore, the selection of suppliers is an area of tremendous importance and should be considered a strategic issue in the effective management of a supply chain. Supplier selection and its related tasks are positioned at the front end in the supply chain process (see Fig. 1). Full-size image (34 K) Fig. 1. The flow process of supply chain management. Figure options During the 1990s, many manufacturers sought to develop strategic alliances with suppliers in order to upgrade their management preference and competitiveness (Kumar et al., 2006 and Shin et al., 2000). While coordination between a manufacturer and its suppliers is typically an important and difficult link in the channel of distribution, many methods have been adopted for supplier selection under rather simplistic perceptions of the decision making process (Chen, Lin, & Huang, 2006). However, supplier evaluation and selection are complicated by the need for decision makers (DMs) to consider various criteria. The selection process mainly involves the evaluation of different criteria and various supplier attributes. This selection process can essentially be considered a multiple criteria decision making (MCDM) problem, which is affected by different tangible and intangible criteria (Pi & Low, 2005). Since 1966, many criteria have been employed to evaluate and select supplier. Dickson (1966) identified 23 different criteria for supplier selection, based on which Weber, Current, and Benton (1991) suggested a number of selection criteria to measure supplier performance, such as price, delivery, quality, productive capability, location, technical capability, management organization, reputation, industry position, financial stability, performance history, and maintainability. Evans (1980) proposed that price, quality and delivery are key criteria for supplier evaluation in the industrial market. Shipley (1985) suggested that supplier selection involve three criteria, namely, quality, price and delivery lead time. Ellram (1990) suggested that in the supplier selection process, firms must to consider whether product quality, offering price, delivery time, and total service quality meet organizational demand. Tam and Tummala (2001) proposed an analytic hierarchy process (AHP) based model and adopted quality, cost, problem-solving capabilities, expertise, delivery lead time, response to customer requests, experience, and reputation in selecting telecommunications systems. Pi and Low (2005) suggested a method for supplier evaluation and selection based on quality, on-time delivery, price, and service quality. Recently, the supplier selection process has received considerable attention in the marketing management literature. Chen et al. (2006) adopted a fuzzy decision making approach to address the supplier selection problem in the supply chain system. Five benefit criteria were considered, including the profitability of supplier, relationship closeness, technological capability, conformance quality, and conflict resolution. Lin and Chang (2008) claimed that communication, reputation, industry position, relationship closeness, customer responsiveness, and conflict-solving capabilities are important criteria in vendor selection. In addition, the role of organizational size in the supplier selection process has been addressed by Wang, Cheng, and Cheng (2009). Table 1 summarizes the criteria that have appeared in literature since 1966; most of the articles referenced above suggest that quality, price, and delivery performance are the most important supplier selection criteria. Table 1. Supplier selection criteria literature review. Selection criteria 1 2 3 4 5 6 7 8 9 10 Price (cost) √ √ √ √ √ √ Product quality √ √ √ √ √ √ √ √ On-time delivery √ √ √ √ √ √ √ Warranty and claims √ After sales service √ √ Technical support/expertise √ Attitude √ Total service quality √ √ Training aids √ Performance history √ √ √ Financial stability √ √ √ √ Location √ √ Labor relations √ Relationship closeness √ √ Management and organization √ √ Conflict/problem solving capability √ √ √ Communication system √ √ Response to customer request Technical capability √ √ √ Production capability √ √ Packaging capability √ Operational controls √ Amount of past business √ Reputation and position in industry √ √ √ √ √ Reciprocal arrangements √ Impression √ Business attempt √ Maintainability √ √ Size √ 1, Dickson (1966); 2, Evans (1980); 3, Shipley (1985); 4, Ellram (1990); 5, Weber et al. (1991); 6, Tam and Tummala (2001); 7, Pi and Low (2005); 8, Chen et al. (2006); 9, Lin and Chang (2008); 10, Wang et al. (2009). Table options Over the years, a number of techniques have been proposed to solve the supplier selection problem. The long list of approaches includes linear programming (LP), mathematical programming models, multiple-objective programming, statistical and probabilistic methods, data envelopment analysis (DEA), cost-based methods (CBM), case-based reasoning (CBR), neural networks (NN), AHP, analytic network process (ANP), fuzzy set theory, and techniques for order preference by similarity to ideal solution (TOPSIS). Recently, the integration of different methodologies to supplier selection process has received considerable attention in the supply chain management literature. Faez, Ghodsypour, and O’brien (2009) presented an integrated fuzzy case-based reasoning and mathematical programming method. Önüt, Kara, and Isik (2009) developed a supplier evaluation approach based on the ANP and TOPSIS methods to help a telecommunication company in vendor selection. Ha and Krishnan (2008) developed a hybrid model that including AHP, DEA and NN approaches to the supplier selection problem. Most recently, Kokangul and Susuz (2009) integrated AHP and mathematical programming to consider both non-linear integer and multiple-objective programming under certain constraints to determine the best suppliers. The integrated model uses source data provided by a manufacturing firm to address a real-world supplier selection problem. In real life, the modeling of many situations may not be sufficient or exact, as the available data are inexact, vague, imprecise and uncertain by nature (Sarami, Mousavi, & Sanayei, 2009). Moreover, the decision making processes that take place in such situations are also based on uncertain and ill-defined information. In the real practice of supplier selection, firms usually confronts with a high degree of uncertainties and fuzziness. Fuzzy set theory is considered the most effective methods in managing vagueness and uncertainty problems. The concept of fuzzy sets was introduced by Zadeh (1965) to mathematically represent data and information possessing non-statistical uncertainties and to provide formalized tools for dealing with imprecision intrinsic to many problems (Kahraman, Cevik, Ates, & Güfer, 2007). In order to model such situations, fuzzy set theory was introduced to express the linguistic terms of decision marking processes. In addition, TOPSIS is a classical MCDM method, and as such it may provide the basis for developing supplier selection models that can effectively handle uncertainty properties. This approach is based on the idea that a chosen alternative should be the shortest distance from the positive-ideal solution and the farthest distance from the negative-ideal solution. Chen et al. (2006) applied linguistic value to measure the ratings and weights of supplier selection criteria and then used a MCDM model based on fuzzy set theory to analyze a supply chain management case. However, their model is only suitable for single-sourcing problems. In the single sourcing scenario, one supplier presumably will satisfy the buyer’s needs; such that the decision must be made on which supplier is the best. In the multi-sourcing case, no supplier can satisfy all the buyer’s requirements, and so more than one supplier can be selected (Ghodsypour & O’Brien, 1998). In this study, an integrated fuzzy TOPSIS and MCGP model is developed to solve multi-sourcing supplier selection problems. First, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Second, a hierarchy multi-model based on fuzzy set theory is expressed and fuzzy positive and negative-ideal solutions are used to find each supplier’s closeness coefficient. Finally, a MCGP model based on the tangible constrains regarding the buyer and its suppliers is constructed and solved to assign order qualities to each supplier. The paper is organized as follows. The next section introduces the basic definitions and notations of fuzzy numbers and linguistic variables. Section 3 presents both the GP and MCGP approaches. Section 4 presents the analytical procedure of the proposed integrated approach. In Section 5, the proposed method is illustrated using a watch company as an example. The finally section presents conclusions and suggestions for future research.
نتیجه گیری انگلیسی
Supplier selection is one of the critical decision-making activities for firms to obtain competitive advantage. To achieve this goal, DMs should apply an effective method and select suitable criteria for supplier selection. Taking both tangible and intangible criteria into account, this paper proposed a novel method, which integrates fuzzy TOPSIS and MCGP, to evaluate suppliers. In a decision-making process, the use of linguistic variables in decision problems is highly beneficial when performance values cannot be expressed by means of numerical values. In general, supplier evaluation and selection problems are vague and uncertain, and so fuzzy set theory helps to convert DM preferences and experiences into meaningful results by applying linguistic values to measure each criterion with respect to every supplier. Employing MCGP enables us to assign order quantities to each supplier and thus maximize the total value of procurement. Given that many multi-choice aspiration levels may exist, a multiple choice method is most appropriate for this type of decision-making. In addition, this integrated method allows for the vague aspirations of DMs to set multiple aspiration levels for supplier selection problems. The integrated advantage of this method is that it allow for the vague aspirations of DMs to set multiple aspiration levels for supplier selection problems in which “the more/higher is better” (e.g., benefit criteria) or “the less/lower is better” (e.g., cost criteria). Furthermore, the proposed method may be useful for various MCDM problems, such as management problems (e.g., project management and location selection) and marketing problems (e.g., new products development and promotion activities) when available data are inexact, vague, imprecise and uncertain by nature.