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.     

Designing a decision support system to evaluate and select suppliers using fuzzy analytic network process

Recent information and communication developments caused that global organizations spread out their markets throughout the world. In this environment, local exclusive markets have been replaced with global competitive ones. Therefore, organizations must concentrate on their main operations to survive in such an environment. To do so, managers have intended to cooperate with some financial partners in long-term relations. In this paper, the aim is to develop a fuzzy analytic network process (ANP) model to evaluate the potential suppliers and select the best one(s) with respect to the vendor important factors. Additionally, ANP is developed by fuzzy sets theory to cover the indeterminacy of decisions made in this field. The authors have augmented the model with a non-linear programming model to elicit eigenvectors from fuzzy comparison matrices. Hybridization of these two concepts can model supplier selection problem in all circumstances and reaches the optimal choice. Finally, a numerical sample is used to validate the proposed model.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. 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 identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

A strategic model using structural equation modeling and fuzzy logic in supplier selection

Supplier selection attained the state of paramount importance for companies in the current scenario because of increasing global competition. Improper selection of suppliers will have an adverse impact on the overall performance of the company. The number of available alternatives in the current market is on a rise, and hence it becomes difficult to select a supplier from among a large lot. An attempt has been made to develop a new composite model using structural equation modeling and fuzzy analytic hierarchy process technique, based on the results of a survey of 151 respondents. Based on the criteria that influence the selection of suppliers, the model has been developed. The study also examines the supplier selection criteria and measures the supplier selection score using the developed model.     

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.     

Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information

Selection of advanced manufacturing technology in manufacturing system management is very important to determining manufacturing system competitiveness. This research develops a fuzzy multiple attribute decision-making applied in the group decision-making to improving advanced manufacturing technology selection process. Since numerous attributes have been considered in evaluating the manufacturing technology suitability, most information available in this stage is subjective, imprecise and vague, fuzzy sets theory provides a mathematical framework for modeling imprecision and vagueness. In the proposed approach, a new fusion method of fuzzy information is developed to managing information assessed in different linguistic scales (multi-granularity linguistic term sets) and numerical scales. The flexible manufacturing system adopted in the Taiwanese bicycle industry is employed in this study to demonstrate the computational process of the proposed method. Finally, sensitivity analysis can be performed to examine that the solution robustness.     

Revisiting the supplier selection problem: An integrated approach for group decision support

Decision support for supplier selection is a highly researched theme in procurement management literature. However applications of group decision support theories are yet to be explored extensively in this domain. This study proposes an approach for group decision support for the supplier selection problem by integrating fuzzy Analytic Hierarchy Process (AHP) for group decision making and fuzzy goal programming for discriminant analysis. In the first step, the fuzzy AHP theory with the Geometric Mean Method has been used to prioritize and aggregate the preferences of a group of decision makers. Then consensus has been developed between these aggregated priorities using the Ordinal Consensus Improvement Approach. Subsequently, the consensual priorities of this group of decision makers have been integrated with fuzzy goal programming theory for discriminant analysis to provide predictive decision support. Finally it has been shown through a case study how the integrated approach using fuzzy AHP for group decision making and fuzzy goal programming with soft constraints has been more effective as compared to an existing approach for group decision making using only AHP.     

Fuzzy AHP approach for supplier selection in a washing machine company

Supplier selection process has gained importance recently, since the cost of raw materials and component parts constitutes the main cost of a product and most of the firms have to spend considerable amount of their revenues on purchasing. Supplier selection is one of the most important decision making problems including both qualitative and quantitative factors to identify suppliers with the highest potential for meeting a firm’s needs consistently and at an acceptable cost. In this study, supplier selection problem of a well-known washing machine company in Turkey is investigated and a fuzzy analytic hierarchy process based methodology is used to select the best supplier firm providing the most customer satisfaction for the criteria determined. After the main attributes and sub-attributes for supplier selection are defined to design the hierarchy structure, the weights of them and alternatives are calculated using fuzzy analytic hierarchy process approach. The supplier with the highest priority weight is selected as the best supplier. Macros in MS Excel are written to calculate the priority weights of the alternatives based on the questionnaire forms used to facilitate comparisons of main attributes, sub-attributes and alternatives.     

A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems

This study described a hybrid model for supporting the personnel selection process in manufacturing systems. Personnel selection is a very important issue for an effective manufacturing system, since the improper personnel might cause many problems affecting productivity, precision, flexibility and quality of the products negatively. On the other hand, selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. In this study, a hybrid model which employs analytic network process (ANP) and modified TOPSIS (Technique for Order Performance by Similarity to Idea Solution) together, is proposed for the personnel selection problem. The ANP is used to analyze the structure of the personnel selection problem and to determine weights of the criteria, and modified TOPSIS method is used to obtain final ranking. To illustrate how the approach is used for the personnel selection problem, an application of a real case in a company is conducted. The application has demonstrated the effectiveness and feasibility of the proposed model. Company management found the application and results satisfactory and implementable in their personnel selection process.     

Fuzzy AHP based prioritization and taxonomy of software process improvement success factors in global software development

The phenomena of global software development (GSD) face many challenges that are specifically associated with software process improvement (SPI). The main objective of this work is to develop the prioritization-based taxonomy of the SPI success factors using the fuzzy AHP approach. Total of twenty-one factors were extracted from the available literature that were further evaluated by conducting questionnaire survey with the SPI experts. In the second stage, multi-criterion decision making (MCDM) fuzzy AHP tool was used to prioritize and develop the taxonomy of the identified factors and their categories. The implications of fuzzy AHP approach are novel in this research area as it has been successfully used previously in different other domains e.g. electrical and electronics, supplier selection, agile software development and personnel selection. The contribution of this study is not only limited to the development of the factor’s taxonomy, but also their proper prioritization by introducing the novel fuzzy AHP approach in the research field of GSD and SPI, which assist to remove the vagueness and uncertainty in the opinion of the process improvement experts.     

Decision-making for the best selection of suppliers by using minor ANP

There are many studies on the application examples and the definitions of the super-matrix in the Analytic Network Process (ANP). The ANP applies network structures with the alternatives and the criteria to clarify a complex decision-making. It is important to obtain the consent from both the criteria and the alternatives because the evaluation is relative and reciprocal. In addition, there are very few studies on the interaction between the criteria and the alternatives. There is no realistic interpretation of the maximum eigenvalue in the ANP and the negative elements of the criteria matrix. Based on our study, the evaluation with only the alternatives consists of the missing values or the non-square matrix, it is not easy to make. The ANP that contains these irregular alternatives is defined as ??Minor ANP??. Then, we describe these methods of making the priority of only the alternative??s matrix values by using the ANP. This study has proven that the missing values in the alternative matrix can be replaced by zeros such that the criteria matrix can be derived without using the inverse alternative matrix. Then, the priority of alternative selection can be obtained through the calculation of eigenvector. An empirical case of the best selection of supplier illustrates our proposed methods for applying to the manufacturing industry. The result reveals the methods can be well adopted in the real world.     

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 hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes

Global economic competition has spurred the manufacturing sector to improve and invest in modern equipment to satisfy the needs of the market. In particular, machine tool selection is the most important problem; it plays a primary role in the improvement of productivity and flexibility in the manufacturing environment and involves the imprecise, vague and uncertain information. This paper presents the hybrid approach of the fuzzy ANP (Analytic Network Process) and COPRAS-G (COmplex PRoportional ASsessment of alternatives with Grey relations) for fuzzy multi-attribute decision-making in evaluating machine tools with consideration of the interactions of the attributes. The fuzzy ANP is used to handle the imprecise, vague and uncertain information from expert judgments and model the interaction, feedback relationships and interdependence among the attributes to determine the weights of the attributes. COPRAS-G is employed to present the preference ratio of the alternatives in interval values with respect to each attribute and calculate the weighted priorities of the machine alternatives. Alternatives are ranked in ascending order by priority. As a demonstration of the proposed model, a numerical example is implemented based on the collected data and the literature. The result is then compared with the rankings provided by other methods such as TOPSIS-G, SAW-G and GRA. Moreover, a sensitivity analysis is conducted to verify the robustness of the ranking. The result highlights that the hybrid approach of the fuzzy ANP and COPRAS-G is a highly flexible tool and reaches an effective decision in machine tool selection.     

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.     

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.     

Intelligent evaluation of supplier bids using a hybrid technique in distributed supply chains

The main idea of this research is to devise the smart module to pick the best supplier bid(s) automatically. The hybrid model is composed of three useful tools: fuzzy logic, AHP, and QFD. The approach has been carefully implemented and verified via a real-world case study in a medium-to-large industry manufacturing vehicle tires and other rubber products. A collection of 12 assessment criteria classified into two categories have been considered. Eight factors are derived from customer suggestions and the other four are design specifications required to manufacture the product. The main outcomes are: a hybrid autonomous model to evaluate supplier bids without direct human intervention; devising a hybrid three-module method and overcoming complexity of computations in resulting algorithm by means of agents; outlining the best criteria to assess suppliers; evaluating the suppliers based on voice of customer during all stages of the process; and discussing analysis, design, and implementation issues of the evaluation agent. The paper includes implications for development of an integrated total system for supply chain coordination. The most important advantages of this work over earlier researches on supplier selection are: implementation of an autonomous assessment mechanism using intelligent agents for the first time, making the best out of three widely applied methodologies all at once, evaluation process mainly based on features of customer order, coordination of supply job based on a bidding system, and portal-mediated operation and control.     

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.     

A fuzzy supplier selection model with the consideration of benefits,opportunities, costs and risks

Even though the research on supplier selection is abundant, the works usually only consider the critical success factors in the buyer–supplier relationship. However, the negative aspects of the buyer–supplier relationship must also be considered simultaneously. The main objective in this study is to propose an analytical approach to select suppliers under a fuzzy environment. A fuzzy analytic hierarchy process (FAHP) model, which incorporates the benefits, opportunities, costs and risks (BOCR) concept, is constructed to evaluate various aspects of suppliers. Multiple factors that are positively or negatively affecting the success of the relationship are analyzed by taking into account experts’ opinion on their importance, and a performance ranking of the suppliers is obtained. TFT-LCD manufacturers in Taiwan, which is the largest TFT-LCD producer country in the world, are facing increasing competition nowadays, and the selection of the most appropriate suppliers for cooperation is essential for firms to achieve competitive advantage. A case study of backlight unit supplier selection for a TFT-LCD manufacturer is presented, and the proposed model is applied to facilitate the decision process. The model is a general form that can be tailored and applied by firms that are making decisions on supplier selection.     

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.     

Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem

Evaluating and selecting a suitable supplier is a complex problem which involves a number of different criteria. In literature, there are various multi-criteria decision making (MCDM) methods available with their own characteristic features. The focus of this study is intuitionistic fuzzy (IF) MCDM methods which have attracted much attention from academics and practitioners in recent years. IF sets are widely used to tackle imprecise and uncertain decision information in decision making due to their capability of accommodating the hesitation in human decision processes. This study proposes a new integrated methodology that is used for the first time in the literature. This approach consists of intuitionistic fuzzy analytic hierarchy process (IFAHP), an MCDM technique, for determining the weights of supplier evaluation criteria, and the concept of intuitionistic fuzzy axiomatic design (IFAD) principles for ranking competing supplier alternatives with respect to their overall performance. Decision makers’ assessments and opinions are extended to the IF environment in this approach and furthermore, the group decision making (GDM) approach is utilized in order to overcome uncertainties and vagueness, minimize the partiality of decision process and to avoid bias. This study contributes to supplier selection and IF sets literature by providing a combined framework based on IFAHP and IFAD methodology for the first time. To assess the validity of the proposed integrated IF MCDM approach, a case study from Turkey is provided. This study can be useful to researchers in better understanding the supplier selection problem theoretically, as well as to organizations in designing better satisfying supplier evaluation systems.