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.     

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.     

An integrated fuzzy MCDM approach for supplier evaluation and selection

A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.     

Supplier selection with different risk preferences and attribute sets: An innovative study based on generalized linguistic term sets

Selecting the optimal supplier is crucial to the management of the company’s supply chain, which has received widespread attention from academia and business circles. Generally, a variety of suppliers and multiple attributes are usually involved in this selection proceeding which can be comparatively regarded as a linguistic multiple attribute group decision making (MAGDM) problem. However, common linguistic MAGDM problems may not take the following characteristics into consideration. Due to the limited knowledge, distinctive interests, and different semantic value expectations of decision makers (DMs), it is necessary for them to consider their diverse risk preferences and use multi-granular linguistic term sets (LTSs) to assess suppliers on individual attribute sets independently. Meanwhile, the complex decision environment may have influences on the integrity of the attribute weight information, such that it is always incompletely known. To deal with the afore-mentioned situations, this paper presents a procedure based on risk preferences and several attribute sets with incomplete weight information for choosing the desirable supplier. Firstly, a new multi-granular fuzzy linguistic transformation model is constructed to normalize linguistic domains of multi-granular generalized linguistic term sets (GLTSs). Multi-granular GLTSs are introduced to describe semantic values of multi-granular LTSs given by DMs with risk preferences. Secondly, according to the maximizing deviation method with incomplete attribute weight information, an optimization model is also established to determine attribute weight vectors of individual attribute sets. Thirdly, a novel method that comprises of the aforesaid models is presented to handle supplier selection problems with risk preferences and several attribute sets. Finally, an illustrative example on supplier selection and comparative analyses are provided to clarify the validity and feasibility of our proposed method. Significantly, the initiation of the proposed method and its application could afford to the theoretical development of linguistic MAGDM, as well as the practical expansion in the domain of supplier selection.     

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 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.     

On vague logics and approximate reasoning based on vague linear transformation

As a generalisation of the Fuzzy Sets theory, vague set has been proven to be a new tool in dealing with vague information. In this article, we attempt to generalise the techniques of fuzzy inference in a vague environment. In the rule-based inference system, an ‘if?…?then?…’ rule can be considered a transformer that implements information conversion between input–output ends. Thus, according to the logical operations of vague linguistic variables, we introduce an approach to approximation inference based on linear transformation, and then discuss the representations for several inference structures regarding single rule, multi-rules and compound rules. By defining the inclusion function of vague sets, we provide vague rough approximation based on measure of inclusion, and then present a method on rule creation from a decision system. A case study on the prediction for welding deformation is used to illustrate the effectiveness of the proposed approaches.     

Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP

Since a company can only perform as well as it is allowed to by its suppliers, the importance of supplier selection in supply chain management has been increasingly recognized. Supplier selection can best be described as a highly complex process, due to the involvement of many, sometimes conflicting, qualitative and quantitative criteria. The objective is to select the most suitable supplier(s) that meet a company’s specific needs. This paper investigates supplier selection in the airline retail industry. We discuss a number of issues that make airline retail complex and distinguish it from conventional retail. The supplier selection problem is solved by means of a two-phased methodology. In the first phase, a conjunctive screening method is used, which aims to reduce the initial set of potential suppliers prior to the comprehensive final choice phase. In the second phase, a fuzzy analytic hierarchy process (AHP) is used, in which suppliers are evaluated against the main criteria and sub-criteria. By combining the decision-maker’s preferences, using the developed methodology will eventually result in a ranking of suppliers that makes it possible to select the most suitable supplier(s). The proposed methodology is applied to one of the largest airlines in Europe, the Royal Dutch Airlines (KLM), and the results are discussed extensively in this paper. We conclude by proposing avenues for future research regarding the general applicability and further extensions.     

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.     

Using linguistic preferences and grey relational analysis to evaluate the environmental knowledge management capacity

This study proposes a combined fuzzy grey relational analysis method based to deal with study objective. This study objective is aimed to present a perception approach to deal with supplier evaluation of environmental knowledge management capacities (EKMC) with uncertainty and lack of information. The ranking of best supplier might be a key strategic direction of other suppliers prior to EKMC. The solving procedure is as follows, (i) the weights of criteria and alternatives are described both in qualitative and quantitative information using fuzzy set theory; (ii) using a grey relational analysis to result the ranking order for all alternatives; (iii) an empirical study of supplier ranking problem in EKMC are used to resolve with this proposed approach and the result indicates that optimal supplier is with higher protection of the environmental knowledge from inappropriate or illegal use or theft (C7) and from the best alternative supplier to study its criteria ranking.     

An integrated method for finding key suppliers in SCM

Association rule is a widely used data mining technique that searches through an entire data set for rules revealing the nature and frequency of relationships or associations between data entities. Supplier selection is a significant work in supply chain management. Often, there will be thousands of potential suppliers and identifying a subset of these suppliers can be a complex process of determining a satisfactory subset based on a number of factors. In this paper, the supplier selection can be viewed as the problem of mining a large database of shipment. The proposed method incorporates the extended association rule algorithm of data mining with that of set theory to find key suppliers. This research has employed a numerical example for the integrated method to develop suitable supplier clusters. The results show that the method is effective and applicable.     

Supplier selection in electronic marketplaces using satisficing and fuzzy AHP

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.     

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.     

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.     

An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry

Globally, supply chains compete in a complex and rapidly changing environment. Hence, sustainable supplier selection has become a decisive variable in the firm’s financial success. This requires reliable tools and techniques to select the best sustainable supplier and enhance understanding about how supplier behavior evolves with time. System dynamics (SD) is an approach to investigate the dynamic behavior in which the system status alterations correspond to the system variable changes. Fuzzy logic usually solves the challenges of imprecise data and ambiguous human judgment. Thus, this work presents a novel modeling approach of integrating information on supplier behavior in fuzzy environment with system dynamics simulation modeling technique which results in a more reliable and responsible decision support system. Supplier behavior with respect to relevant sustainability criteria in the past, current and future time horizons were sourced through expert interviews and simulated in Vensim to select the best possible sustainable supplier. Simulation results show that an increase in the rate of investment in sustainability by the different suppliers causes an exponential increase in total sustainability performance of the suppliers. Also, the growth rate of the total performance of suppliers outruns their rate of investment in sustainability after about 12 months. A dynamic multi-criteria decision making model was presented to compare results from the systems dynamics model.     

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.     

Co-development of product and supplier platform

The platform strategy has been implemented to efficiently manage the increased variety in products and manufacturing systems domains by achieving their effective and rapid re-configuration. Despite the increased development of platforms research, their back-end issues such as the supply chain and supplier selection have received little attention. In this research, a methodology that integrates the product platform synthesis with the selection of suppliers to form a supplier platform is introduced. The formed supplier platform is a collection of suppliers capable of supplying the components/modules of the product platform. The supplier platform remains unchanged for product generations, and non-platform suppliers are added or removed as needed for producing different product variants in different production periods. The presented co-development methodology consists of three phases. First, co-platforming is used to map the product requirements to the supplier’s domain; then an intuitionistic fuzzy TOPSIS method is employed to assign weights to the suppliers according to selected criteria. The suppliers are chosen next and their platform is synthesized. A laptop product family is used to illustrate the developed methodology. The significance of this research is the synthesis of a supplier platform which can be used without change for many product variants and many product generations. Its implementation enables the planning and creation of strategic alliances with the product platform suppliers.     

An integrated fuzzy-lp approach for a supplier selection problem in supply chain management

In supply chain management process, the firm select best supplier takes the competitive advantage to other companies. Thus, supplier selection is an important issue and with the multiple criteria decision-making approach, the supplier selection problem includes both tangible and intangible factors. This paper is aimed to present an integrated fuzzy and linear programming approach to the problem. Firstly, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Then a hierarchy multiple 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 linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities assigned to each supplier according to the linear programming model. The integrated model is illustrated by an example in a textile firm.     

Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study

Supplier selection is a decision-making process to identify and evaluate suppliers for making contracts. Here, we use interval type-2 fuzzy values to show the decision makers’ preferences and also introduce a new formula to compute the distance between two interval type-2 fuzzy sets. The performance of the proposed distance formula in comparison with the normalized Hamming, normalized Hamming based on the Hausdorff metric, normalized Euclidean and the signed distances is evaluated. The results show that the signed distance has the same trend as our method, but the other three methods are not appropriate for interval type-2 fuzzy sets. Using this approach, we propose a hierarchical clustering-based method to solve a supplier selection problem and find the proximity of the suppliers. To illustrate the applicability of the proposed method, first a case study of supplier selection problem with 8 criteria and 8 suppliers are illustrated and next, an example taken from the literature is worked through. Then, to test the hierarchical clustering-based method and compare with the obtained results by two other methods, a comparative study using experimental analysis is designed. The results show that while the proposed hierarchical clustering algorithm provides acceptable results, it is also conveniently appropriate for using interval type-2 fuzzy sets and obtaining proximity of suppliers.     

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.