An application of the fuzzy ELECTRE method for supplier selection

Supplier selection is vital to the success of a manufacturing firm. Supplier selection is a multi-criteria decision-making problem and is of strategic importance for most companies. As the conventional methods for supplier selection are inadequate for dealing with the imprecise or vague nature of linguistic assessment, a new method called the fuzzy technique for ELECTRE (ELimination Et Choix Traduisant la REalité) is proposed. The aim of this study is to compare and contrast crisp and fuzzy ELECTRE methods for supplier selection. The proposed methods are applied to a manufacturing company in Turkey. After determining the criteria that affect the supplier selection decisions, the results for both crisp and fuzzy ELECTRE methods are presented.     

Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection

Decision-making techniques are used to help evaluate the current suppliers’ aim at classifying performance of individual suppliers against desired levels of performance, so as to design suitable plans to increase the performance and capabilities of suppliers. In this study, an integrated model is introduced and proposed for increasing the supplier selection and evaluation quality. The methodology is composed of two steps. The first stage is fuzzy decision-making trial and evaluation laboratory method in which the interactions between the evaluation criteria and the criteria weight have been computed. At the second stage, performances of suppliers are assessed using both the criteria weights obtained at the first stage and fuzzy c-means clustering algorithm by classifying the vendors according to their performances. Obtained results show that the proposed model is very well suited as a decision-making tool for supplier selection decisions.     

Fuzzy hybrid decision model for supplier evaluation and selection

This paper proposes a structured, integrated decision model for evaluating suppliers by combining the fuzzy analytical hierarchy process (FAHP) and grey relational analysis (GRA). The qualitative and partially-known information is incorporated in this decision model using the fuzzy set theory. In this proposed methodology, the weights of the evaluation criteria are calculated by using FAHP, then the ranking of the suppliers is determined by using GRA. Finally to show the robustness of the model, a sensitivity analysis is also performed. In this study, the supplier selection problem of an electroplating industry in the southern part of India was investigated, demonstrating the effectiveness of this developed integrated model. This model can help in solving the complex decision in supplier selection practice. The results generated from the model are properly validated and finally a systematic solution with decision support is provided for decision makers. This model can be integrated with other decision support systems of similar kinds of industries.     

A distinct decision model for the evaluation and selection of a supplier for a chemical processing industry

The supplier selection process has gained importance recently due to the considerable amount of revenue spent on purchasing. The intention of this work is to develop an appropriate hybrid model by integrating the analytical hierarchy process (AHP) and grey relational analysis (GRA) for supplier evaluation and selection, which comprises three stages. In Stage I, the most influential criteria are selected by mutual-information-based feature selection. Stage II focuses on the determination of the weights of the attributes using AHP, while Stage III is used for the determination of the best supplier using GRA. The proposed model is illustrated using the case study of an electroplating industry to highlight the effectiveness and flexibility of the model. The model effectively combines specialised knowledge, experience and quantitative data to select the best suppliers. This paper presents the model development, solution and application processes of the proposed hybrid model for supplier selection. The decision support software was implemented in Excel to automate supplier selection. The proposed hybrid model is applied to enhance the decision-making process in supplier selection and also helps decision makers to effectively select suppliers.     

Pre-selection of suppliers through an integrated fuzzy analytic hierarchy process and max-min methodology

There are a variety of analytical models for supplier selection ranging from simple weighted techniques to complex mathematical programming approaches. However, these models are specifically aimed at supporting a decision maker in a single phase, especially in the final selection phase and they have failed to consider the supplier selection process from a holistic point of view. Although the methodology presented in this paper primarily focused on the prequalification of potential suppliers, the outputs of the previous phases, namely problem definition and formulation of criteria, are used as inputs in this methodology. The methodology utilises a fuzzy analytic hierarchy process (AHP) method to determine the weights of the pre-selected decision criteria, a max-min approach to maximise and minimise the supplier performances against these weighted criteria, and a non-parametric statistical test to identify an effective supplier set. This information supports decision makers in making the final selection with effective alternative choices. Potential application of the proposed methodology is demonstrated in Audio Electronics in Turkey's electronics industry.     

Global supplier selection: a fuzzy-AHP approach

Global supplier selection has a critical effect on the competitiveness of the entire supply chain network. Research results indicate that the supplier selection process appears to be the most significant variable in deciding the success of the supply chain. It helps in achieving high quality products at lower cost with higher customer satisfaction. Apart from the common criteria such as cost and quality, this paper also discusses some of the important decision variables which can play a critical role in case of the international sourcing. The importance of the political-economic situation, geographical location, infrastructure, financial background, performance history, risk factors, etc., have also been pointed out in particularly in the case of global supplier selection. Supplier selection problem related to the global sourcing is more complex than the general domestic sourcing and as a result it needs more critical analysis, which could not be found properly in past available literatures. This paper discusses the fuzzy based Analytic Hierarchy Process (fuzzy-AHP) to efficiently tackle both quantitative and qualitative decision factors involved in selection of global supplier in current business scenario. The fuzzy-AHP is an efficient tool to tackle the fuzziness of the data involved in deciding the preferences of the different decision variables involved in the process of global supplier selection. The triangular fuzzy numbers are used to transform the linguistic comparison of the different decision criteria, sub-criteria and performance of the alternative suppliers. The pairwise comparison matrices help in deciding the synthetic extent value of each comparison and finally, the priority weights of one alternative over another are decided in this paper. An example from a manufacturing industry searching for the global supplier for a critical component is used to demonstrate the effective implementation procedure of proposed fuzzy-AHP technique. The proposed model can provide the guidelines and directions for the decision makers to effectively select their global suppliers in the current competitive business scenario.     

A novel approach for supplier selection based on the Kano model and fuzzy MCDM

This paper proposes a three-phase approach for supplier selection based on the Kano model and fuzzy Multi Criteria Decision-Making. Since the supplier selection problem involves different criteria, quality attributes have been assumed to denote the importance weight of the criteria for supplier selection. Furthermore, to consider the inherent vagueness of human thought, a fuzzy logic has been utilised. Initially, the importance weight of the criteria has been calculated using a fuzzy Kano questionnaire and fuzzy analytic hierarchy process. In the second phase, the Fuzzy TOPSIS technique has been used to screen out in capable suppliers. Finally, in the third phase, the filtered suppliers which are qualified, once again will be evaluated by the same approach for the final ranking. The proposed approach has also been examined in a case study.     

An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis

Selecting a proper machine tool is one of the important decisions a company has to make. Companies which fail to do so face many problems which negatively affect the firm's productivity, flexibility, precision and its responsiveness capabilities. Selection of a machine tool involves a lot of criteria to be simultaneously studied and so it requires a multi-criterion decision making (MCDM) method to solve it. Also the subjectivity involved in such decisions ask for the use of theories such as fuzzy and grey which are very effective in handling subjective inputs. This paper integrates the fuzzy analytic hierarchy process (AHP) and grey relational analysis approaches for the selection of a machine tool from a given set of alternatives. Fuzzy AHP is used to calculate the priority weights of the criteria. Subsequently grey relational analysis (GRA) is employed to rank the alternatives. A well known problem existing in literature has been picked up for the numerical illustration. The results obtained in this paper are better when compared with that existing in literature.     

An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D

The selection of the outsourcing manufacturing partners (OMPs) is an important issue for research and development (R&D) in the pharmaceutical industry. The selection process considers several main criteria, as well as a few sub-criteria for each main criterion. Such problems can be formulated as hierarchical structures and can usually be resolved with multi-criteria decision-making approaches. This paper presents an integrated fuzzy approach for selecting a suitable OMP in pharmaceutical R&D. In the integrated approach, fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process. Fuzzy AHP and fuzzy TOPSIS are included in the integrated approach. Fuzzy AHP is used to determine the fuzzy weights of criteria and sub-criteria because it can effectively determine various criteria's weights in a hierarchical structure. Fuzzy TOPSIS aims to find the OMP with the best performance with respect to the sub-criteria. We exemplify the integrated approach using a numerical application to demonstrate its feasibility.     

A utility-driven approach to supplier evaluation and selection: empirical validation of an integrated solution framework

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model.     

A supplier selection model: a comparison of fuzzy logic and the analytic hierarchy process

This paper aims to compare two tools for decision makers that intend to support the decision of the selection of the appropriate supplier. Suppliers are crucial to both the efficiency and effectiveness of the performance of companies. A critical success factor of these companies is the selection of the appropriate supplier. A methodology is proposed to optimise the evaluation process based on different criteria. The proposed approach extends the one proposed by Ordoobadi (2009 Ordoobadi, SM. 2009. Development of a supplier selection model using fuzzy logic. Supply Chain Management: An International Journal, 14(4): 314–327. [Crossref], [Web of Science ®] , [Google Scholar], Development of a supplier selection model using fuzzy logic. Supply Chain Management: An International Journal, 14 (4), 314–327) who proposed the application of fuzzy logic (FL) where we use the same example case study in order to compare the analytic hierarch process (AHP) with FL. In this paper we demonstrate how we can achieve the same objective of expressing human assessments in the form of linguistic expressions by using AHP. Moreover, we demonstrate the capability to run a sensitivity analysis which helps to understand the causal relationships among the different factors. We demonstrate how this capability can help us to explain and predict the different relationships among criteria and alternatives. Moreover, we provide a measure that is able to capture the consistency of the decision maker's preferences. In our approach we provide a single unit of scale that is not only capable of ranking suppliers but also provides an understanding of the difference in scale between different suppliers which can then help to allocate resources accordingly. These facilities are not offered by Ordoobadi (2009 Ordoobadi, SM. 2009. Development of a supplier selection model using fuzzy logic. Supply Chain Management: An International Journal, 14(4): 314–327. [Crossref], [Web of Science ®] , [Google Scholar]). The proposed approach here can help companies to identify the best supplier in changing environments. The paper describes a decision model that incorporates a decision maker's subjective assessments and applies a multiple criteria decision making technique to manipulate and quantify these assessments. Unlike many similar studies, two techniques have been performed on the same case study in order to improve our understanding of the differences in the proposed techniques.     

A new DEA common-weight multi-criteria decision-making approach for technology selection

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.     

A rule-based multi-criteria approach to inventory classification

The traditional inventory classification method classifies stock keeping units (SKUs) to three classes based on their annual dollar usage, while in real world problems, other criteria are important as well. In this paper, considering multi-criteria situations, a simple, effective and practical rule-based method is designed and implemented in a real world case, using MATLAB software. The most important characteristic of the proposed method is taking into account the inherent ambiguities that exist in the reasoning process of the system of classification. The methodology and the method proposed here may be easily implemented by inventory managers. The results obtained from the case study in this paper are compared with the analytic hierarchy process (AHP) method. Finally concluding remarks and suggestions for future work are provided.     

An integrated weighted fuzzy multi-objective model for supplier selection and order scheduling in a supply chain

This paper presents a new weighted fuzzy multi-objective model to integrated supplier selection, order quantity allocation and customer order scheduling problem to prepare a responsive and order-oriented supply chain in a make-to-order manufacturing system. Total cost and quality of purchased parts as well as the reliability of on-time delivery of customer orders are regarded as the objectives of the model. On the other hand, flexible suppliers can contribute to the responsiveness and flexibility of entire supply chain in the face of uncertain customer orders. Therefore, a mathematical measure is developed for evaluating the volume flexibility of suppliers and is considered as the other objective of the model. Furthermore, by considering the effect of interdependencies between the selection criteria and to handle inconsistent and uncertain judgments, a fuzzy analytic network process method is used to identify top suppliers and consider as the last objective. In order to optimise these objectives, the decision-maker needs to decide from which supplier to purchase parts needed to assemble the customer orders, how to allocate the demand for parts between the selected suppliers, and how to schedule the customer orders for assembled products over the planning time horizon. Numerical examples are presented and computational analysis is reported.     

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.     

Interval-valued availability framework for supplier selection based on component importance

The availability of ageing systems, particularly weapon systems within the Department of Defense, is of significant concern, as budgets tighten and system replacement is infeasible. This work addresses the selection of sole suppliers according to their ability to provide component parts that strengthen availability of the system. We extend a popular multi-criteria decision-making approach, Technique for Order Preferences by Similarity to an Ideal Solution, by (i) considering the availability of individual components as the criteria in the decision problem and (ii) weighting those criteria according to the value of component importance measures while (iii) accounting for uncertainty in underlying reliability and maintainability parameters with interval numbers. An aircraft example illustrates the approach.     

Maintenance supplier selection considering life cycle costs and risks: a fuzzy goal programming approach

In this study, we address a new variant of supplier selection problem named maintenance supplier selection problem faced by a manufacturer. The production system consists of different multi-component equipments whose maintenance activities require several components (parts) each of which could be provided by multiple suppliers. A multi-objective mathematical model is developed to decide about the supply base of each part as well as the purchasing quantity of each part from each selected supplier. The model accounts for the total life cycle costs of purchased parts and various risks threatening the candidate suppliers. A fuzzy/soft lexicographic goal programming approach with soft priorities between objectives is proposed to enable the decision-maker to make preferred trade-offs between objectives by which the effects of various risks in each phase of life cycle of procured parts are investigated. The capability and effectiveness of the proposed model is validated through a case study. Some sensitivity analyses are also carried out for investigating the impact of cost, risk and objectives’ priorities on the final preferred compromise solution. Finally, some managerial insights and concluding remarks are provided.     

A hierarchical fuzzy TOPSIS approach to assess improvement areas when implementing green supply chain initiatives

Implementing green supply chain management (GSCM) initiatives can generate more business opportunities for firms. It also requires changes in firms’ operational capabilities and resources that may have an adverse effect on firms’ operations performance. In order to achieve sustainable economic and environmental performance, it is essential for companies to evaluate different green initiatives and assess improvement areas when implementing green initiatives. This study proposes a fuzzy hierarchical TOPSIS approach to support such an assessment. It enables decision makers to better understand the complete evaluation process and provide a more accurate, effective and systematic decision support tool. An illustrative case is presented to help researchers and practitioners understand the importance of developing an appropriate organisation strategy in implementing green practices.     

Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method

In this paper, we present a fuzzy multiple criteria decision making (FMCDM) model known as fuzzy balancing and ranking. In contrast to other MCDM models, our proposed model does not require the weights of decision making criteria. First, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular fuzzy numbers. The foregoing model obtains the alternative rankings through a four-stage process. Second, an outranking matrix is derived indicating that the frequency with which one alternative is superior to all other alternatives based on each criterion. Third, the outranking matrix is triangularised to obtain an implicit pre-ordering or provisional order of alternatives. Fourth, the provisional order of alternatives is subjected to various screening and balancing operations that require sequential application of a balancing principle to the so-called advantages–disadvantages table that combines the criteria with the pair-wise comparisons of alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding the problem of supplier selection.     

A novel fuzzy group decision-making approach to prioritising engineering characteristics in QFD under uncertainties

In new product development, design teams commonly need to define engineering characteristics (ECs) in a quality function deployment (QFD) planning process. Prioritising the engineering characteristics in QFD is essential to properly plan resource allocation. However, the inherent vagueness or impreciseness in QFD presents a special challenge to the effective calculation of the importance of ECs. Generally, there are two types of uncertain input in the QFD process: human perception and customer heterogeneity. Many contributions have been made on methods to prioritise ECs. However, most previous studies only address one of the two types of uncertainties that could affect the robustness of prioritising ECs. To address the two types of uncertainties simultaneously, a novel fuzzy group decision-making method that integrates a fuzzy weighted average method with a consensus ordinal ranking technique is proposed. An example is presented to illustrate the effectiveness of the proposed approach. Results of the implementation indicate that the robustness of prioritising ECs based on the proposed approach is better than that based on the method of Chen et al. (Chen, Y., Fung, R.Y.K., Tang, J.F., 2006. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research, 174 (3), 1553–1556).