Sustainable supplier selection: A ranking model based on fuzzy inference system

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers’ opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers’ performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.     

An integrated fuzzy synthetic evaluation approach for supplier selection based on analytic network process

In order to ensure the uninterrupted supply of items, the suppliers’ performance needs to be evaluated periodically. The evaluation process typically consists of identifying the attributes and criteria relevant to the decision, and measuring the performance of a supplier by considering the relevant criteria. But the evaluation process is complex. Linguistic assessment of suppliers may be carried out based on several criteria. Much of the data are difficult to obtain and ambiguous or vague to interpret. Nonetheless, a rational process of evaluation must exist to select the most appropriate suppliers. This paper develops a supplier evaluation approach based on the analytic network process (ANP) and fuzzy synthetic evaluation under a fuzzy environment. The importance weights of various criteria are considered as linguistic variables. These linguistic ratings can be expressed in triangular fuzzy numbers by using the fuzzy extent analysis. Fuzzy synthetic evaluation is used to select a supplier alternative and the Fuzzy ANP (FANP) method is applied to calculate the importance of the criteria weights. Then an integrated FANP and fuzzy synthetic evaluation methodology is proposed for evaluating and selecting the most suitable suppliers. A hypothetical example is presented and the results indicated that the combination of ANP and fuzzy synthetic evaluation provided useful tool to select the optimal supplier.     

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.     

An intelligent supplier evaluation,selection and development system

Supplier evaluation and selection process has a critical role and significant impact on purchasing management in supply chain. It is also a complex multiple criteria decision making problem which is affected by several conflicting factors. Due to multiple criteria effects the evaluation and selection process, deciding which criteria have the most critical roles in decision making is a very important step for supplier selection, evaluation and particularly development. With this study, a hybridization of fuzzy c-means (FCM) and rough set theory (RST) techniques is proposed as a new solution for supplier selection, evaluation and development problem. First the vendors are clustered with FCM algorithm then the formed clusters are represented by their prototypes that are used for labeling the clusters. RST is used at the next step of modeling where we discover the primary features in other words the core evaluation criteria of the suppliers and extract the decision rules for characterizing the clusters. The obtained results show that the proposed method not only selects the best supplier(s), also clusters all of the vendors with respect to fuzzy similarity degrees, decides the most critical criteria for supplier evaluation and extracts the decision rules about data.     

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.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

A knowledge-based supplier intelligence retrieval system for outsource manufacturing

Knowledge management is to promote business success through a formal, structured initiative to improve the use of knowledge in an organization, in which an effective organizational memory information system plays an increasingly important role. Unlike the past, the performance of an enterprise now depends much on the performance and relationship of its customer–suppliers in the value chain. Good customer–supplier relationships are important for an organization to respond to dynamic and unpredictable changes. This paper describes a knowledge-based supplier selection and evaluation system, which is a case-based reasoning decision support system for outsourcing operations at Honeywell Consumer Products (Hong Kong) Limited in China. As a result, collaborative suppliers are identified quickly during the new product development process. By using the system, the cumulative performance of suppliers is constantly updated automatically according to past practice. This means that the knowledge of suppliers can be retained, categorized, retrieved and managed effectively.     

A multiple criteria supplier segmentation using outranking and value function methods

Suppliers play a key role in supply chain management which involves evaluation for supplier selection problem, as well as other complex issues that companies should take into account. The purpose of this research is to develop and test an integrated system, which allows qualifying providers and also supplier segmentation by monitoring their performance based on a multiple criteria tool for systematic decision making. This proposal consists in a general procedure to assess suppliers based mainly on exploiting all reliable databases of the company. Firstly, for each group of products, their evaluation criteria are defined collaboratively in order to determine their critical and strategic performance, which are then integrated with other criteria that are specific of the suppliers and represent relevant aspects for the company, also classified by critical and strategic dimensions. Two multiple criteria methods, compensatory and non-compensatory, are used and compared so as to point out their strengths, weaknesses and flexibility for the supplier evaluation in different contexts, which are usually relevant in the supply chain management. A value function approach is the appropriate method to qualify providers to be included in the panel of approved suppliers of the company as this process depends only on own features of the supplier. On the other hand, outranking methods such as PROMETHEE have shown greater potential and robustness to develop portfolios with suppliers that should be partners of the company, as well as to identify other types of relationships, such as long term contracts, market policies or to highlight those to be removed from their portfolio. These results and conclusions are based on an empirical research in a multinational company for food, pharmaceuticals and chemicals. This system has shown a great impact as it represents the first supplier segmentation proposal applied to industry, in which decision making not only takes into account opinions and judgements, but also integrates historical data and expert knowledge. This approach provides a robust support system to inform operative, tactical and strategic decisions, which is very relevant when applying an advanced management in practice.     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

A fuzzy ANP model for supplier selection as applied to IC packaging

Supplier selection has been a popular topic since the selection of the most appropriate supplier for cooperation is being increasingly important for the success of an enterprise. The problem is multi-criteria in nature, and a variety of multi-criteria decision making methodologies have been proposed. However, most of them did not take into account the interrelationships among the critical success factors and the fuzziness of the data involved in deciding the preferences of the factors. The objective of this research is to propose a fuzzy analytic network process model to evaluate various aspects of suppliers. With the consultation with the experts, the proposed model can consider the feedback and interdependency of factors in a network, and the factors are pairwise compared under an uncertain environment. The weights of factors can be calculated, and the final priority of suppliers obtained. Semiconductor industry becomes increasingly globalize competitive nowadays, and a good supply chain relationship is essential for a company to survive and to acquire reasonable profit. Thus, a case study of IC packaging company selection in Taiwan is presented, and the proposed model is applied to facilitate the decision process. The priorities of the factors and the ranking of the suppliers can be a recommendation or reference for decision makers when making a supplier evaluation and selection decision.     

基于数据挖掘的油田企业供应商选择

选择合适的供应商,有效控制油田企业成本,是油田企业提升盈利水平、持续健康发展的重中之重。在大数据时代下,伴随着供应链全球化的趋势日益增强,供应商的数量愈加巨大,以往对供应商的选择方式已经不适用于企业科学有效地选择供应商的需求。文章基于数据挖掘的理念,研究某油田企业现有的某一领域供应商评价指标分值,并以此作为数据样本,应用聚类分析中的K-means算法对供应商评价指标分值进行聚类分析。将供应商进行分类,挖掘供应商评价指标分值的分布特点及规律,构建适合油田企业的科学、规范、高效的供应商选择体系,摒弃传统选择方式无法避免的主观性、随机性和偶然性等弊端,为油田企业决策者挑选最合适的供应商作为战略合作伙伴提供了科学、有效的依据。     

Designing an integrated AHP based decision support system for supplier selection in automotive industry

PurposeThe purpose of this paper is to propose a decision support model for supplier selection based on analytic hierarchy process (AHP) using a case of automotive industry in a developing country of Pakistan and further performs sensitivity analysis to check the robustness of the supplier selection decision.MethodologyThe model starts by identifying the main criteria (price, quality, delivery and service) using literature review and ranking the main criteria based on experts’ opinions using AHP. The second stage in the adopted methodology is the identification of sub criteria and ranking them on the basis of main criteria. Lastly perform sensitivity analysis to check the robustness of the decision using Expert Choice^ۛ software.FindingsThe suppliers are selected and ranked based on sub criteria. Sensitivity analysis suggests the effects of changes in the main criteria on the suppliers ranking. The use of AHP in the supplier selection gives the decision maker the confidence of the consistency and the robustness throughout the process.Practical implicationsThe AHP methodology adopted in this study provides managers in automotive industry in Pakistan with the insights of the various factors that need to be considered while selecting suppliers for their organizations. The selected approach also aids them in prioritizing the criterion. Managers can utilize the hierarchical structure of adopted supplier selection methodology suggested in this study to rank the suppliers on the basis of various factors/criteria.Originality/valueThis study makes three novel contributions in supplier selection area. First, AHP is applied to automotive industry and use of AHP in the supplier selection gives decision maker the confidence of the consistency. Second, sensitivity analysis enables in understanding the effects of changes in the main criteria on the suppliers ranking and help decision maker to check the robustness throughout the process. Last, we find it important to come with a simple methodology for managers of automotive industry so that they can select the best suppliers. Moreover, this approach will also help managers in dividing the complex decision making problem into simpler hierarchy.     

基于熵的第三方物流供应商选择评估模型

应用模糊聚类算法和熵权多目标决策法建立评估模型,以实现物流服务供应商选择的优中选优。其中,模糊聚类法对所有可供选择的供应商进行聚类分析,确定各物流服务供应商优势或特点,以获得满足客户物流服务需求的供应商类作为备选供应商;应用熵权多目标决策法计算备选供应商与理想点的距离及贴近度,并按量化的计算结果对备选供应商进行优先权排序,从而为企业选择第三方物流供应商提供可参考的模型和理论依据。     

A green supplier selection model for high-tech industry

With growing worldwide awareness of environmental protection, green production has become an important issue for almost every manufacturer and will determine the sustainability of a manufacturer in the long term. A performance evaluation system for green suppliers thus is necessary to determine the suitability of suppliers to cooperate with the firm. While the works on the evaluation and/or selection of suppliers are abundant, those that concern environmental issues are rather limited. Therefore, in this study, a model for evaluating green suppliers is proposed. The Delphi method is applied first to differentiate the criteria for evaluating traditional suppliers and green suppliers. A hierarchy is constructed next to help evaluate the importance of the selected criteria and the performance of green suppliers. Since experts may not identify the importance of factors clearly, the results of questionnaires may be biased. To consider the vagueness of experts’ opinions, the fuzzy extended analytic hierarchy process is exploited. With the proposed model, manufacturers can have a better understanding of the capabilities that a green supplier must possess and can evaluate and select the most suitable green supplier for cooperation.     

A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method.     

A fuzzy-Bayesian model for supplier selection

The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making process is approached as a static one rather than a highly dynamic process. Delays, lack of capacity, quality related issues are common examples of dynamic aspects that have a direct impact on long-term relationships with suppliers. This paper presents a novel method based on the integration of influence diagram and fuzzy logic to rank and evaluate suppliers. The model was developed to support managers in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting criteria, to study the sensitivity of the behavior of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. To be effective, the computational implementation of the method was embedded into an information system that includes several functionalities such as supply chain simulation and supplier’s databases. A case study in the biodiesel supply chain illustrates the effectiveness of the developed method.     

A fuzzy multi-criteria decision making model for supplier selection

This paper presents a hybrid fuzzy model for group Multi Criteria Decision Making (MCDM). A modified fuzzy DEMATEL model is presented to deal with the influential relationship between the evaluation criteria. The modified DEMATEL captures such relationship and divides the criteria into two groups, particularly, the cause group and the effect group. The cause group has an influence on the effect group where such influence is used to estimate the criteria weights. In addition, a modified TOPSIS model is proposed to evaluate the criteria against each alternative. Here, a fuzzy distance measure is used in which the distance from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated. The resulted distances were used to calculate the similarity to Ideal and Anti-ideal points. Later, an optimal membership degree (closeness coefficient) of each alternative is computed to estimate to which extent an alternative belongs to both FPIS and FNIS. The closer the degree of membership to FPIS and the farther from FNIS the more preferred the alternative. The membership degree is obtained by the optimization of a defined objective function that measures the degree to which an alternative is similar/dissimilar to the Ideal/Anti-Ideal solutions. The closeness coefficient is used to rank the alternatives. To better have a high contrast between the ranks of alternatives an optimization problem was introduced and solved to maximize the contrast.The presented hybrid model was applied on an industrial case study for the selection of cans supplier/suppliers at Nutridar Factory in Amman-Jordan to demonstrate the proposed model. Finally a sensitivity analysis is introduced to verify the resulting ranks of the available suppliers via testing different values of the used parameters. The sensitivity analysis has shown robust and valid results that are close to real preferences of the consulted experts.     

Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for multi-criteria decision making in supplier evaluation and selection problem. The contemporary supply-chain management is looking for both quantitative and qualitative measures other than just getting the lowest price. After evaluating a number of distinct suppliers, determining the reliable suppliers by ANFIS model with better approximation will support decision makers. To this end, ANFIS is evaluated for different data sets with the attributes of the suppliers and their scores that are gathered from a previous study conducted for the same problem under the name of Neural Network (NN) application for fuzzy multi-criteria decision-making model. In the proposed ANFIS model built for determining supplier score, linear regression analysis (R-value) and Mean Square Error (MSE) were 0.8467 and 0.0134, respectively, while they were 0.7733 and 0.0193 for NN for fuzzy. ANFIS gives better results according to MSEs. Hence, it is determined that ANFIS algorithm can be used in multi-criteria decision making problems for supplier evaluation and selection with more precise and reliable results.     

An ERP model for supplier selection in electronics industry

Asus Tech. is the largest motherboard manufacturer in Taiwan. Hundreds of suppliers cooperate with the company in business. So that supplier selection is the most important function of a purchasing department in the enterprise. An enterprise resource planning (ERP) system in the process of supplier selection may result in the great savings in both costs and man hours. In the concept of push and pull, an ERP system acts as an efficient tool in the resource integration and profit creation for a company. Through ERP, a decision manager can clearly realize the strength and weakness of the purchasing operation. To establish a real-time purchasing environment, a methodology of analytic network process (ANP), technique for order preference by similarity to ideal solution (TOPSIS) and linear programming (LP) are effectively applied in the supplier selection process. ANP and TOPSIS are used to calculate the weight and give suppliers a ranking; LP effectively allocates order quantity to each vendor. As to the result, four PC board suppliers are given orders for 1200, 727, 1000 and 73 pieces.     

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross‐efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross‐efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria.