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Supplier selection has emerged as an important activity regarding strategic purchasing with implications for the operational efficiency of both organisations and supply chains. Given the need to evaluate both qualitative and quantitative criteria for different supply alternatives, the decision-making process became more complex.

AbstractIn a multi-channel distribution environment, the selection and assessment of suppliers are pivotal to effective retailer performance management. This necessitates heightened qualifications and competence from suppliers. This study examines the relationship between the perception of supplier firms’ status, evaluation performance, and overall satisfaction of food buyers with suppliers in online/offline channels. In addition, this study investigates the moderating impacts of the distribution channel to explore the influence of the online/offline distribution channel on this relationship. For exploratory research, food buyers from 20 large retailers headquartered in Seoul, South Korea, were surveyed, and 110 valid questionnaires were used for analysis. Results of the study indicate that among suppliers’ firm factors, suppliers’ firm size and experience with the retailer were significantly and positively related to buyer satisfaction. Likewise, among evaluation factors, product competency and delivery reliability were significantly and positively related to buyer satisfaction. In addition, the results reveal that the distribution channel has moderating effects on the relationship between firm size and buyer satisfaction, as well as between experience with the retailer and buyer satisfaction. Therefore, the study verified that the distribution channel is an important factor that must be considered not only in the consumer market but also in the buying behavior research of the business-to-business market.

Supply chain management (SCM) is crucial in establishing long-term partnerships that are pivotal for achieving sustained business success. Effective SCM demands rigorous criteria and decision-making processes, which significantly impact the overall outcomes. Recent studies highlight cloud-based market analysis as a valuable tool for assessing supply chain dynamics, offering insights into the benefits and challenges of SCM. The integration of deep learning (DL) and machine learning (ML) approaches in SCM presents transformative potential, enabling more efficient management of the supply chain. This paper identifies the contributions of DL and ML techniques in various aspects of SCM, including supplier selection, production, inventory control, transportation, demand and sales estimation, and others. The extensive review presented in this work delivers an in-depth examination of the integration of DL and ML with SCM, highlighting strategies for enhancing operational efficiency, addressing current limitations, and identifying future research opportunities. A comprehensive literature table consolidates existing research on enhancing SCM with ML and DL techniques, offering a precise overview of objectives, findings, and areas for improvement, and providing rapid insights into the evolving landscape of SCM.

Purpose – This study aims to examine the role of supplier selection and supplier monitoring in public procurement efficiency in terms of cost reduction in Tanzania. Design/methodology/approach – A structured questionnaire was used to collect cross-sectional survey data from 179 public procuring entities in Tanzania. Structural equation modelling (SEM) was used to analyse the collected data. Findings – The findings revealed that supplier selection and supplier monitoring are positive and significant predictors of public procurement efficiency in terms of cost reduction. Research limitations/implications – This study was conducted in Tanzanian public procurement contexts, so generalisations should be made with caution. Also, this study collected cross-sectional data; other studies may consider longitudinal data. Practical implications – This study provides procurement practitioners with insights into selecting the proper suppliers and embracing supplier monitoring to achieve procurement efficiency in terms of cost reduction. Originality/value – This study examines the effects of supplier selection and supplier monitoring on procurement cost reduction as a measure of public procurement efficiency in the Tanzanian context. Consequently, it provides empirical evidence of supplier management practices in the public procurement context.

Green purchasing is a form of implementing selection to buy products by prioritizing environmentally friendly principles. Green purchasing of kitchen equipment is important to help reduce environmental impacts and guarantee the safety, stability, welfare, and quality of life of the present and future generations. This research was conducted at Anantara Uluwatu Bali Resort located on Pemutih Street, Labuan Sait, Uluwatu, Bali. This study aims to determine the implementation of green purchasing in the procurement of kitchen equipment at Anantara Uluwatu Bali Resort which was analyzed using green purchasing indicators developed by Pramesti. The data collection method used is interviews with key informants determined purposively and observation techniques using participant observation. The data analysis method used is the qualitative descriptive analysis technique. The results of this study indicate that Anantara Uluwatu Bali Resort has not implemented green purchasing optimally in the procurement of kitchen equipment. There are several obstacles experienced by the purchasing department, namely not having guidelines for implementing green purchasing, the price of goods is expensive, and the production of goods is still small so it cannot meet the needs of kitchen items. The implication of this research is to advise companies to make Standard Operating Procedures regarding implementing green purchasing and to carry out routine price evaluations for suppliers with environmentally friendly product criteria to get prices that match the company’s budget. In addition, the purchasing department can also make regular observations on goods produced by suppliers with environmentally friendly criteria.

Public universities have recognized the power they hold as influential entities within communities, and with this power comes the responsibility to select suppliers who share on their vision. By prioritizing sustainability in their procurement strategies, universities can lessen their environmental impact and serve as examples for other organizations. Embracing sustainability in supplier selection requires the acknowledgement of the interrelatedness between economic, social, and environmental factors. It mandates organizations to consider long-term consequences of traditional purchasing considerations, such as cost and quality, rather than short-term gains. However, the absence of clear guidelines and policies on sustainable supplier selection results in inefficiencies in decision-making within most universities. Consequently, their ability to prioritize partnerships with eco-friendly, socially responsible, and economically viable suppliers is hindered. The purpose of this study was to analyze the impact of sustainable supplier selection (SSS) on the procurement performance of public universities. A cross-sectional research design was employed, utilizing a population of 40 employees from the procurement department, selected from ten chartered public universities in Kenya. The findings revealed that public universities have embraced sustainable supplier selection, although to varying degrees. Moreover, the coefficient of determination was 0.472 implying that the sustainable supplier selection criteria used by the universities accounted for 47.2% of their procurement performance. The study constitutes a model utility for fostering sustainable procurement practices within the confines of public universities. It recommends the development of robust approaches to evaluating supplier environmental and social performance that could significantly heighten business sustainability levels.

The supply chain has gained prominence in recent years, both in specialized literature and in economic practice, being defined and analysed as an operational component of the behaviour of economic actors, both those with global activities and those with localized activities. The main aspects related to this thematic area are associated with complex concepts that have a quantifiable economic impact in terms of money, time, quality, and competitiveness. Supplier selection has become a matter of strategic partnership within global supply chains enabling customer performance and competitiveness, thus increasing supply chain leaders’ awareness to develop and implement complex tools and metrics for supplier comparison before business award, one of the representative being net present value comparison. Net Present Value is defined as the sum of the investment's expected cash inflows and outflows discounted back to their present value at a company-agreed risk-adjusted rate. This study is focused on showing how an assessment of comparative net present values can clarify a business case and improve the decision making process towards a particular supplier selection, together with meeting project budget, internal targets, financial risk assessment, and strategic planning.

Management of variability in raw materials is critical in food manufacturing systems, where the variability of input attributes is a result of its biological nature. We present a methodology for the analysis of such production systems that provides a means for assessing tactical and strategic issues. To illustrate our method we partner with a large food manufacturer whose primary raw material is wheat flour to examine how variation in raw material attributes and the use of manufacturing controls impact the firm's finished goods quality and net revenue outcomes. At the strategic level, we investigate the supplier selection problem where the firm must choose which supplier criterion to emphasize: lower price or higher quality. We find that the firm's operations fall short of achieving optimal net revenue, which would represent an increase of 65%. We also show that flour price should be the driving force in supplier selection for our firm..

Supply chain management (SCM) has gradually evolved beyond the straightforward logic of benefits and economic viewpoints. Supplier selection and performance evaluation are the crucial strategic components of any SCM system with a substantial economic impact and risk reduction. Several conflicting factors make supplier selection a challenging multi-criteria decision-making problem. This paper introduces a method called alternative ranking with the elected nominee (ARWEN) to select suppliers in Iran’s dairy product chain store. The primary principle of ARWEN is to choose the best alternative based on the lowest change rate rather than the elected nominee. Four extensions of the ARWEN method are proposed depending upon the nature and level of information available to the decision-makers. A fifth extended version termed E-ARWEN is also recommended to consider the negative form of the elected nominee. Two novel statistical tools, the ranking performance index and the Zakeri-Konstantas distance product correlation coefficient, are also put forth to validate the ARWEN extensions’ outcomes. The results and verification of this new method are carried out through two supplier selection case examples. Comprehensive comparisons were carried out to explore the new methods’ behaviors, indicating ARWEN III and E-ARWEN have similar behavior to VIKOR, SAW, and EDAS in generating rankings.

Supplier selection is a critical process that directly affects overall firms’ performance. There are various factors that come into play when firms are establishing supplier selection criteria. One of the key factors that directly influence firms’ decision towards establishing supplier selection criteria is their relationships with current and previous suppliers. In this study, we qualitatively explore the impact of few decision biases that generates from firms’ relationship with existing or previous suppliers on firms’ supplier selection criteria for new suppliers. Focus group interviews were used as a tool to collect qualitative data. The data was analyzed, and results revealed that the selection criteria for new supplier is affected by reference point bias in decision making based on failed relationship between firm and a supplier.

Under intense industry competition, decision makers must ensure that products demanded by consumers can be quickly produced with minimum production cost. However, because uncertainties are unavoidable and inevitably affect decision makers, numerous studies have discussed how to control uncertainties or minimize their effects. Multiple uncertainties that interact simultaneously may cause a combined effect in actual systems. Therefore, this study presents a robust optimization model with two uncertainties, extending the method of robust optimization with one uncertainty. To demonstrate the applicability of the proposed model with two uncertainties, this study uses the supplier selection problem with component purchase quantity allocation in supply chain management as an analysis case. This considers the reliability of production and transportation and develops a multi-objective robust optimization model with two uncertainties. In addition, a nondominated sorting genetic algorithm is proposed for solving the proposed multi-objective robust optimization model. The relationship between price of robustness and budget parameters is explored by considering the robust optimization model with production and transportation uncertainties proposed in this study. Finally, there is a comparative analysis between the results for price of robustness in the proposed two-uncertainty model and in the one-uncertainty model. First published online 15 December 2022

With the rise of the concept of environmental protection and the attention of all sectors of society to the ecological environment, the selection of green suppliers is a hot topic. In this paper, we develop the combinative distance-based assessment (CODAS) method in the probabilistic hesitant fuzzy sets (PHFSs) to cope with the multiple attributes group decision making (MAGDM). A standardized approach that integrates multiple methods is applied to normalize the original data. Moreover, the statistics variance (SV) method is applied under PHFSs to calculate the objective weighting vector of evaluation criteria. In the end, a case for supplier selection and the comparative analysis are used to confirm the feasibility and utility of this new approach. First published online 30 August 2022

The research aimed to determine the appropriate weight criteria and sub-criteria in selecting the chemical solvent supplier. It was also to recommend the best alternative suppliers using a comparative analysis method of Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). The research was conducted in one of the chemical manufacturing companies in Indonesia. The type of research was descriptive with a quantitative approach that used primary data sources such as questionnaires, interviews, and observations, and secondary data such as books and journals. The criteria studied were cost, quality, delivery, flexibility, supplier profile, and supplier relationship. The results of using FAHP indicate that the criteria with a major influence on the decision making of supplier selection are quality followed by delivery, cost, supplier relationship, flexibility, and supplier profile in sequence. Then, using FTOPSIS, it recommends that the best supplier alternative decision is Supplier A with a weight value of 0,773 and an approved evaluation status. The results have shown that both methods are suitable for supplier selection, particularly in supporting group decision making and modeling uncertainty.

In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method.

Supplier selection is the process by which firms identify, evaluate and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm's operational and financial resources and is considered as an important determinant of the success of its supply chain. In spite of being strategically important to organizations, the decision for supplier selection is often complex and unstructured. Furthermore, it is inherently a multi-criterion decision-making (MCDM) problem, which pertains to structuring and solving decision problems involving multiple criteria. The paper provides a framework to analyze and evaluate supplier selection in Indian pharmaceutical sector (IPS) using MCDM technique of fuzzy analytic hierarchy process. It intends to improve managerial decision-making in the IPS in developing a supplier selection strategy based on multi-criteria evaluation technique. [ABSTRACT FROM AUTHOR]

Sustainable supplier selection plays an important role in sustainable supply chain management operations and implementation. In this paper a novel formal modeling approach is conceptually developed and presented to address sustainable supplier selection. Grey theory and TOPSIS, a distance based multiple criteria method, are used for the integration and evaluation of sustainable supplier performance for sustainable supplier selection. From a research perspective, TOPSIS is improved to more effectively deal with grey numbers by integrating a degree of likelihood rather than converting grey numbers into crisp numbers functions, and it provides more flexible supplier rankings. This methodology strengthens the sustainable supplier selection process, and can be applied to other multiple criteria decision making problems. Illustrative calculations are made using data on sustainable supplier selection and evaluation published by Bai and Sarkis (2010). The technique is relatively accurate, matching well with results from a published grey rough set approach. The methodology easily implementable with minimal complex calculations required. It can also provide support for sustainable supplier selection, benchmarking, and improvement decisions. This is one of the first papers to integrate a broad set of sustainability factors for grey-based TOPSIS and supplier selection.

Purpose: When choosing a supplier, many companies give priority to the lowest cost of products. Problems arise when the consumer wants to buy products with several better quality characteristics, but the products have only one of them. The aim is to establish a link between the quality characteristics of the product and its market value in terms of consumer benefits. Methodology/Approach: The proposed model is based on determining the importance of criteria that determine the indicators of quality, reliability, characteristics of products and their comparison with the market price at which these products are supplied to the consumer. The model is built on the basis of statistical processing of the data received from consumers about preferences at the choice of production. Findings: The method of determining the optimal supplier based on the cost and quality indicators of the product. Some indicators of the quality of products and their ranking by significance for the consumer are given. It gives the examples of calculation methods for univariate and two-factor analysis. It shows the ways of diagramming the determination of values of factors of production. Research Limitation/implication: The model is relevant only at close market value of production for the consumer. Originality/Value of paper: The methodology of criteria-based evaluation of the quality indicators of the supplied products or services allows making a selection of a products supplier on the basis of quality characteristics of the supplied products.

Background: Increasing competition among companies in the oil and gas industry results in a greater need for efficient processes. In Brazil, most oil operations are owned by a single company, which has a public trade structure. The company requires a large number of suppliers, including suppliers of raw materials, equipment and services. In fact, the company needs to employ experts to assist in the supplier selection process in order to be able to choose the best available alternatives. Therefore, supplier selection has been seen to have a significant impact on the success or otherwise of the company´s ventures. Objectives: This article proposes a group decision multi-criteria model for selecting suppliers for a Brazilian oil company. Method: It is proposed to use the PROMETHEE II method to obtain decision-makers’ individual evaluations and the voting procedure by quartiles so as to convert the individual positions into a position for the group. Results: An application of the model is performed to demonstrate the feasibility of the model in addressing supplier selection problems in a context with multiple stakeholders. Combining the results from the individual analysis of the PROMETHEE II method with the voting by quartiles procedure allowed individual preferences to be aggregated into a result that was representative of the group and produced a ranking of suppliers for one of the type of services used by the company. Conclusion: The results were very satisfactory, demonstrating that it is possible to apply the model to a context featuring additional decision-makers and alternatives.

In the past decade, global competition are forcing firms to increase their level of outsourcing for raw or semi-finished products and building long term relationship with their supply chain partners. The objective is to present a wide-ranging decision making technique for ranking supplier alternatives in view of the effect of selected criteria. A proposed method is developed aiming the usage of Fuzzy-Rasch model applying five point Likert scale for criteria weight and Grey based COmplex PRoportional ASsessment (COPRAS-G) method for evaluating and ranking the potential alternatives, as per criteria. The applicability of the induced methodology for supplier selection problem in all environments is shown through a case study in telecommunication sector. A sensitivity analysis is performed based on changing weight patterns of criteria to show the stability in ranking result of the proposed approach. Further, a comparative analysis between the ranking results of proposed method done with existing grey multi-attribute decision-making methods viz. VIKOR-G, ARAS-G and TOPSIS-G using spearman’s correlation coefficient for checking the reliability of the ranking result.

The available integrated models for choosing efficient suppliers developed so far are mostly specific to companies with mass production capabilities. However, in some sectors involved in project-type manufacturing, the same decision-making criteria cannot be applied and, plus, there is no point in determining the quantity of orders. For instance, in wind power plant projects, a single turbine supplier needs to be selected for each project. This study proposes an integrated FANP-f-MIGP model that ensures the selection of the optimal supplier for each project by applying the model to an energy firm. The criteria specific to the selection of wind power plant turbine suppliers are established, and the criteria weights are obtained by fuzzy analytic network process (FANP). As a result of the analysis, the most important criterion of all is cost. These weights constitute the coefficients of the f-MIGP model’s objective function. Under the defined constraints, by minimizing cost and risk and maximizing quality and services of the firm, the selection of an optimal wind turbine supplier from three suppliers for each of three projects is ensured. This study contributes to the literature both by the specific criteria it establishes and its proposed integrated model which allows for the selection of the best supplier in wind turbine and similar project-based productions.

Purpose: Most original equipment manufacturers (OEMs) are strategically involved in supplier base rationalization and increased consciousness of sustainable development thus, reinforcing need for accurately considering sustainability in supplier selection to improve organizational performance. In real industrial case, imprecise data, ambiguity of human judgment, uncertainty among sustainability factors and the need to capture all subjective and objective criteria are unavoidable and pose huge challenge to accurately incorporate sustainability factors into supplier selection.Methodology: This study develops a model based on integrated multi- criteria decision making (MCDM) methods to solve such problems. The developed model applies Fuzzy logic, DEMATEL and TOPSIS to effectively analyze the interdependencies between sustainability criteria and to select the best sustainable supplier in fuzzy environment while capturing all subjective and objective criteria. A case study is illustrated to test the proposed model in a gear manufacturing company, an OEM to provide insights and for practical applications.Findings: Results show that social factors of sustainability ranks as the most important in supplier selection. However, the most influential sustainability sub- criteria are work safety (WS) and quality.Originality/Value: The model is capable of capturing all subjective and objective criteria in fuzzy environment to accurately incorporate sustainability factors in supplier selection. It is decision support tool relevant for providing insights to managers while implementing sustainable supplier selection.

Since supplier selection is an important part in management fields, this research focuses on weighted non-hierarchical fuzzy model to increase supply chain management performance. Supplier selection researches have significantly increased but most of methods have been focused on hierarchical determination of indices. This article by use of a multiple objective function tried to present a method that can consider non-hierarchical determination of indices in specific conditions. This research by use of deliverable indices of supply chain management tries to select the best suppliers. In this paper it is assumed that all suppliers have the ability to supply needed items but client can only make a product they provide. Quality of supply chain deliverable, supply chain reliability and supply chain visibility names indices have been selected to increase efficiency in the supply chain. This approach presents local optimal solutions by use of a heuristic logic in supply chain management. These indices are used as fuzzy to select the appropriate suppliers. By this fuzzy method, appropriate supplier can be set for each of the items. The presented approach have been introduced a weighted indices to determine best supplier in specific conditions. In this research weighted non-hierarchical fuzzy sets have been used to select appropriate suppliers. This method is useful for supplier selection problems.

Evaluation and selection of candidate suppliers has become a major decision in business activities around the world. In this paper, a new hybrid approach based on integration of Gene Expression Programming (GEP) with Data Envelopment Analysis (DEA) (DEA-GEP) is presented to overcome the supplier selection problem. First, suppliers’ efficiencies are obtained through applying DEA. Then, the suppliers’ related data are utilized to train GEP to find the best trained DEA-GEP algorithm for predicting efficiency score of Decision Making Units (DMUs). The aforementioned data is also used to train Artificial Neural Network (ANN) to predict efficiency scores of DMUs. The proposed hybrid DEA-GEP is compared to integrated approach of Artificial Neural Network with Data Envelopment Analysis (DEA-ANN) to support the validity of the proposed model. The obtained results clearly show that the model based on GEP not only is more accurate than the DEA-ANN model, but also presents a mathematical function for efficiency score based on input and output data set. Finally, a real-life supplier selection problem is presented to show the applicability of the proposed hybrid DEA-GEP model. First published online: 29 Jun 2016

Supplier selection and order allocation, a longstanding challenge in supply chain management, has recently begun incorporating risk minimization alongside cost, reflecting growing interest in supply chain resilience and risk mitigation. In response, hybrid frameworks leveraging artificial intelligence and machine learning have emerged. However, current methods often lack mechanisms to update decisions over time and typically rely solely on demand forecasts. To address these gaps, this study introduces a new hybrid approach that integrates machine learning–based predictions of supplier delivery delays into a linear programming model for multiperiod supplier selection and order allocation. Additionally, the proposed method evaluates a continuous training strategy, wherein predictions and decisions are refreshed as new data become available. Empirical evidence from an automotive case study demonstrates that this approach reduces prediction errors and total costs more effectively than models without continuous training, albeit with increased order allocation instability.

The increasing importance of considering environmental issues as a part of the corporate social responsibility, which has environmental and social impacts as well as affects the image and competitiveness of a firm, has encouraged companies to revise their major processes of supply chain management (SCM). Since the performance of a company heavily relies on its suppliers, it is of vital importance to incorporate environmental criteria into supplier selection problem to satisfy both conventional and environmental criteria, which have only been considered by a limited number of studies. Therefore, following a brief review of green supply chain management (GSCM) and sustainable supply chain management (SSCM) concepts and investigation of supplier selection approaches, this study after focuses on a modified Delphi method that has been applied to determine supplier selection criteria. Also, the intuitionistic fuzzy value (IFV) and interval value intuitionistic fuzzy (IVIF) have been utilised for supplier evaluation regarding the subjective nature and uncertainty of judgment. By using the Choquet Integral operator and fuzzy measures, the best supplier has been selected, and the comparison between IFV and IVIF has been made. This methodology has been applied to a manufacturing company to assess the applicability of the proposed methodology. The proposed methodology can be used for real world problems that contain fuzziness or interacting decision criteria. Moreover, due to a high level of expert involvement in the decision-making process, we claimed that the knowledge of experts has been utilised constructively.

This paper presents a multi-criteria decision making model used for supplier selection for software development outsourcing on e-marketplaces. This model can be used in auctions. The supplier selection process becomes complex and difficult on last twenty years since the Internet plays an important role in business management. Companies have to concentrate their efforts on their core activities and the others activities should be realized by outsourcing. They can achieve significant cost reduction by using e-marketplaces in their purchase process and by using decision support systems on supplier selection. In the literature were proposed many approaches for supplier evaluation and selection process. The performance of potential suppliers is evaluated using multi criteria decision making methods rather than considering a single factor cost.

Supplier selection is a critical multi-criterion decision-making activity for suc- cessful supply chain management. This study involved developing an integrated supplier selection methodology, which is constructed using analytic network process, data envelop- ment analysis, and multiple objective particle swarm optimization. The proposed integrated methodology can account for multiple supplier selection criteria and set boundaries on weight value for multiple objective data envelopment analysis inputs and outputs. To solve the data envelopment analysis model, a new algorithm based on multiple objective particle swarm optimization is introduced, which embeds with tabu list and group mechanisms, and then, it is found to be superior to the compared algorithms in solving performance on three test functions and the illustrative case. In addition, the proposed integrated method- ology was applied to a supplier selection problem of sphygmomanometer manufacturer in Taiwan to verify its applicability of decision-making process. The results show that the methodology can be implemented as an effective decision aid for supplier selection under multiple criteria with weight restrictions.

In the analytical hierarchy process (AHP), it needs the decision maker to establish a pairwise comparison matrix requires n(n–1)/2 judgments for a level with n criteria (or alternatives). In some instances, the decision maker may have to deal with the problems in which only partial information and uncertain preference relation is available. Consequently, the decision maker may provide interval fuzzy preference relation with incomplete information. In this paper, we focus our attention on the investigation of incomplete interval fuzzy preference relation. We first extend a characterization to the interval fuzzy preference relation which is based on the additive transitivity property. Using the characterization, we propose a method to construct interval additive consistent fuzzy preference relations from a set of n–1 preference data. The study reveals that the proposed method can not only alleviate the comparisons, but also ensure interval preference relations with the additive consistent property. We also develop a novel procedure to deal with the analytic hierarchy problem for group decision making with incomplete interval fuzzy preference relations. Finally, a numerical example is illustrated and a supplier selection case in supply chain management is investigated using the proposed method. First published online: 05 Feb 2014

Supplier selection is a good strategy for firms that can reduce operating costs and improve competitiveness for computer, communication and consumer electronics (3C) industry. The major aim of this research is to build a systematic approach for establishing a supplier selection model, and then prioritize improvement criteria in order to best supply chain management. The study proposed a hybrid approach by using the interpretive structural modeling (ISM) method to deal with the interrelationship among criteria, and the analytic network process (ANP) method is employed to recognize the criteria of supplier selection and evaluate with respect to environmental competency for the case of Taiwan's 3C industry. The study shows that the proposed model could be an effective and efficient decision-making tool that can be easily extended to other contexts. Especially, it has provided decision-makers and researchers with better understanding of the differences in supplier selection activity needs and specific management interventions by examining these criteria.