数据挖掘在供应商选择中的应用

针对供应链国际化趋势的背景下,传统供应商选择方法已无法实现对供应商进行合理选取要求,以建立一套适合企业的、科学的、合理的供应商评价体系来进行供应商选择为目标,从供应商评价指标体系建立的原则、建立过程及各评价指标的计算方法几方面对BP神经网络分类方法进行介绍,阐述了运用数据挖掘知识从大量的供应商中选择出最适合企业的供应商的方法和过程。     

图书供应商选择的混合型多属性群决策

针对同时存在基数评价信息与序数偏好的情形,考虑每个属性下的决策者权重,为图书供应商选择建立混合型多属性群决策模型。对定性和定量属性分别用0-1规划和线性加权和法求出决策群体对各供应商的评价值,再用0-1规划求得决策群体对供应商的最终排序结果。最后以一个实例说明模型的应用。此方法可提高供应商选择的科学性和有效性。     

基于主成分分析与BP神经网络的供应商选择模型

供应商选择是企业进行决策的重要内容,也直接影响着企业竞争力。在科学合理的构建供应商评价指标体系的基础上,首先对供应商评价的数据进行主成分分析,然后建立基于BP神经网络的供应商评价模型,最后以实例验证。这两种方法相结合不仅简化了模型结构,而且较好的克服评价指标主观性强的问题,为供应商选择提供了一种新的、实用的评价方法。     

集成化服务供应链的物流服务商选择研究

在分析集成化服务供应链的基础上,从客户满意度、服务质量、服务成本、企业资质、协同能力和绿色竞争力六个方面,构建了比较全面客观的物流服务供应商评价指标体系。通过熵权计算出物流服务供应商权值向量和专家打分计算出各个二级指标的权重,构建定性和定量相结合的供应商评价模型,通过综合评价计算出各个物流服务供应商的评价总权值。最后,结合案例说明,建立的服务供应商评价指标体系和综合评价模型能够比较全面地评价供应商,为研究物流供应商选择提供有益参考价值。     

两阶段多供应商选择采购模型

针对如何从众多供应商中选择出适合需求的供货商进行准时采购这一问题,提出两阶段多供应商选择采购模型。利用层次分析方法对各个供应商按照定性准则进行分析评价,利用定量准则所建立的多供应商选择采购的成本优化模型对初步选择的供应商做进一步选择,从而确定最终的供货商及供货数量。实际应用结果表明,该模型不但能使企业选择出符合要求的供货商,而且能使企业降低采购成本和产品成本。     

基于支持向量机的供应商评价模型

提出了利用支持向量机建立供应商评价系统的方法．给出了供应商评价指标体系及具体量化方法，采用支持向量机的1-v-1分类策略建立了供应商的评价模型．最后通过仿真实验证明了基于支持向量机的供应商评估系统的可行性和实用性．仿真表明，支持向量机在小样本情况下具有良好的非线性建模能力和泛化能力．     

电子商务供应链环境中供应商选择研究

电子商务的迅速发展对于企业供应商选择有着重要和深远的影响,但传统的供应商评价存在着各种不足之处,本文提出了利用层次分析法模型科学的处理定量和定性评价指标,选出最佳的合作伙伴.     

ERP软件质量模糊综合评价方法

为给ERP软件供应商和应用ERP软件的企业提供一种定量的ERP软件质量评价方法，基于Waiters和McCall3层软件质量度量模型，建立了适用于ERP软件的质量评价模型。研究了运用模糊理论对ERP软件质量进行评价的方法。通过模糊运算获得了ERP软件质量的评价结果。通过一个典型ERP软件实例，验证了该方法的有效性和可行性。     

评价波动对石化项目采购决策的影响

石化项目物资采购决策的本质是供应商选择,主要依据企业按照物资供应商一定评价标准所得到的评价考核结果.由于物资供应商评价标准中的主观因素和时间、环境因素可能导致评价考核结果变化,使得物资供应商的评价会产生波动,导致不同的物资供应商评价结果,从而影响采购决策.为了研究物资供应商评价波动对采购决策的影响程度,以典型石化项目—乙烯裂解炉项目物资采购为例,提出了供应商量化考核因子的变量,建立以采购为主的供应链数学规划模型.通过定量分析,对比不同物资供应商量化考核因子的波动对供应商选择的影响大小,识别出对供应商量化考核因子变化更为敏感的物资类别,为做好石化项目物资供应商选择和采购决策提供有益参考.     

基于AHP-FCE的供应商选择问题研究与应用

文中在对现有供应商选择方法分析的基础上,利用层次分析法在确定各指标的权重方面以及综合模糊评价法在处理多指标多层次的综合评价问题方面的优点,提出了基于层次分析法和综合模糊评价法相结合的供应商选择方法,并且详细描述了供应商选择的步骤。该方法将定性和定量分析有机地结合起来,能够充分体现评价因素和评价过程的模糊性,减少了个体主观臆断所带来的弊端。经过在钢材采购管理决策支持系统中的应用,证明了该方法符合客观实际,评价结果可信、可靠。     

基于结构熵权—灰关联的绿色供货商评价与选择研究*

为了解决评价选择过程中的不确定性、复杂性等问题,将定性与定量方法相结合,建立了供货商评价与选择指标体系,并提出了一种基于结构熵权—灰关联的绿色供货商评价决策模型。基于熵理论,采用主、客观赋值结合的结构熵权法获得指标综合权重;再使用模糊评判和灰关联,获得结果的关联度排序;最后借助算例验证了模型的有效性,为评价绿色供货商提供了有效的方法。     

A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection

Supplier selection has become a very critical activity to the performance of organizations and supply chains. Studies presented in the literature propose the use of the methods Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy AHP (Fuzzy Analytic Hierarchy Process) to aid the supplier selection decision process. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of these two methods in the context of supplier selection decision making. The comparison was made based on the factors: adequacy to changes of alternatives or criteria; agility in the decision process; computational complexity; adequacy to support group decision making; the number of alternative suppliers and criteria; and modeling of uncertainty. As an illustrative example, both methods were applied to the selection of suppliers of a company in the automotive production chain. In addition, computational tests were performed considering several scenarios of supplier selection. The results have shown that both methods are suitable for the problem of supplier selection, particularly to supporting group decision making and modeling of uncertainty. However, the comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers. Thus, this comparative study contributes to helping researchers and practitioners to choose more effective approaches for supplier selection. Suggestions of further work are also proposed so as to make these methods more adequate to the problem of supplier selection.     

Group decision making process for supplier selection with VIKOR under fuzzy environment

During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system. A numerical example is proposed to illustrate an application of the proposed model.     

A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules

Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase.     

An novel approach to supplier selection based on vague sets group decision

In practice, in purchasing decision-making, many quantitative and qualitative factors, with vagueness and imprecision, have to be considered. This makes the decision process very complicated and unstructured. Besides the fuzzy sets theory, vague sets theory is one of the methods used to deal with uncertain information. Since vague sets can provide more information than fuzzy sets, it is considered superior in mathematical analysis of uncertain information. In this paper, a new approach based on vague sets group decision is proposed to deal with the supplier selection problem in supply chain systems. The work procedure is shown briefly, as follows: First, linguistic values are used to assess the ratings and weights for quantitative or qualitative factors. Second, degree of similarity and probability of vague sets are used to determine the ranking order of all alternatives. Finally, a numerical example of the selection problem of suppliers is shown, to highlight the procedure of the proposed approach, at the end of this paper.     

An intelligent approach to supplier evaluation in automotive sector

During the process of supplier evaluation, selecting the best desirable supplier is one of the most critical problems of companies since improperly selected suppliers may cause losing time, cost and market share of a company. For this multiple-criteria decision making selection problem, in this paper, a fuzzy extension of analytic network process (ANP), which uses uncertain human preferences as input information in the decision-making process, is applied since conventional methods such as analytic hierarchy process cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. The resulting fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgments. In short, in this paper, an intelligent approach to supplier selection problem through fuzzy ANP is proposed by taking into consideration quantitative and qualitative elements to evaluate supplier alternatives, and a case study in automotive sector is presented.     

Supplier selection in a fuzzy group setting: A method using grey related analysis and Dempster–Shafer theory

Supplier selection in a fuzzy group setting is a very important strategic decision involving decisions balancing a number of conflicting criteria and opinions from different experts. This paper uses grey related analysis and Dempster–Shafer theory to deal with this fuzzy group decision making problem. First, in the individual aggregation, grey related analysis is employed as a means to reflect uncertainty in multi-attribute models through interval numbers. Second, in the group aggregation, the Dempster–Shafer (D–S) rule of combination is used to aggregate individual preferences into a collective preference, by which the candidate alternatives are ranked and the best alternative(s) are obtained. The proposed approach uses both quantitative and qualitative data for international supplier selection. It provides alternative tools to evaluate and improve supplier selection decisions in an uncertain global market.     

An integrated supplier selection methodology incorporating QFD and DEA with imprecise data

Supplier evaluation and selection is an important group decision making problem that involves not only quantitative criteria but also qualitative factors incorporating vagueness and imprecision. This paper proposes a novel fuzzy multi-criteria group decision making framework for supplier selection integrating quality function deployment (QFD) and data envelopment analysis (DEA). The proposed methodology allows for considering the impacts of inner dependence among supplier assessment criteria through constructing a house of quality (HOQ). The lower and upper bounds of the weights of supplier assessment criteria are identified by adopting fuzzy weighted average (FWA) method that enables the fusion of imprecise and subjective information expressed as linguistic variables. An imprecise DEA methodology is implemented for supplier selection, which employs the weights of supplier assessment criteria computed by FWA utilizing the data from the HOQ and the supplier ratings with respect to supplier assessment criteria. The application of the proposed framework is demonstrated through a case study in a private hospital in Istanbul.     

Structured methodology for supplier selection and evaluation in a supply chain

Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture.In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology.This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.     

Combining Bayesian Networks and Total Cost of Ownership method for supplier selection analysis

In this study, we analyze the supplier selection process by combining Bayesian Networks (BN) and Total Cost of Ownership (TCO) methods. The proposed approach aims to efficiently incorporate and exploit the buyer’s domain-specific information when the buyer has both limited and uncertain information regarding the supplier. This study examines uncertainty from a total cost perspective, with regards to causes of supplier performance and capability on buyer’s organization. The proposed approach is assessed and tested in automotive industry for tier-1 supplier for selecting its own suppliers. To efficiently facilitate expert opinions, we form factors to represent and explain various supplier selection criteria and to reduce complexity. The case study in automotive industry shows several advantages of the proposed method. A BN approach facilitates a more insightful evaluation and selection of alternatives given its semantics for decision making. The buyer can also make an accurate cost estimation that are specifically linked with suppliers’ performance. Both buyer and supplier have clear vision to reduce costs and to improve the relations.