Approach For Supplier Selection Research Articles

A Novel Fuzzy-Prospect Theory Approach for Hydrogen Fuel Cell Component Supplier Selection for Automotive Industry

A Novel Fuzzy-Prospect Theory Approach for Hydrogen Fuel Cell Component Supplier Selection for Automotive Industry

A Robust Decision-Making Approach for Supplier Selection Using Complex Picture Fuzzy Information Involving Prioritization of Attributes

A Robust Decision-Making Approach for Supplier Selection Using Complex Picture Fuzzy Information Involving Prioritization of Attributes

A Novel MDCM Approach for Sustainable Supplier Selection in Healthcare System in the Era of Logistics 4.0

The COVID-19 pandemic has led to major disruptions in workflows across all industries. All sectors are trying to sustain operations during this extremely difficult time and the healthcare sector is the most important of them. It is unthinkable to stop the operations of the health system because it serves human life. Health institutions must supply the products such as masks, gloves, and ventilators subject to service on time for certain activities to continue indefinitely under all conditions. By adopting modern logistics activities and technologies, healthcare organizations can provide sustainable diagnosis and treatments to patients by automating their various operations. With the COVID-19 pandemic, how to select an appropriate sustainable supplier has become an important task in the era of Logistics 4.0. From this viewpoint, a sustainable supplier selection framework is implemented for a health institution under the effect of the pandemic. To determine the direct effects of the pandemic in the health sector, fuzzy Multi-Criteria Decision-Making (MCDM) methods are utilized in the application. After a thorough review of the literature and interviews with experts, the criteria are organized in a comprehensive hierarchical structure. The fuzzy Best-Worst Method (F-BWM) technique is employed to find the weights for the determined criteria. Consequently, the fuzzy Additive Ratio Assessment Method (F-ARAS) method was applied to rank the alternative suppliers. In addition, with a comprehensive sensitivity analysis, alternative situations are examined against possible breaks in the supply chain. Thus, from the perspective of Logistics 4.0 and sustainability, this study contributes to the literature with an analysis of the health system’s survival in difficult and fragile periods, such as COVID-19. Investigating the importance of SSS can be a road map for the policymakers and the decision-makers is beneficial since the impact of COVID-19 on SSS is studied from the perspective of Logistics 4.0.

An integrated intuitionistic fuzzy set and stochastic multi-criteria acceptability analysis approach for supplier selection

An integrated intuitionistic fuzzy set and stochastic multi-criteria acceptability analysis approach for supplier selection

An approach for reliable circular supplier selection and circular closed-loop supply chain network design focusing on the collaborative costs, shortage, and circular criteria

An approach for reliable circular supplier selection and circular closed-loop supply chain network design focusing on the collaborative costs, shortage, and circular criteria

An Integrated Two-Dimension Linguistic Intuitionistic Fuzzy Decision-Making Approach for Unmanned Aerial Vehicle Supplier Selection

With the rapid development of unmanned aerial vehicles (UAVs) and their applications in problems such as power line inspection, selecting the appropriate UAV supplier according to several sustainable attributes has attracted many interests. In this regard, an integrated multiattribute group decision-making (MAGDM) method based on the best-worst method (BWM) and MULTIMOORA method with two-dimension linguistic intuitionistic fuzzy variables (2DLIFVs) is proposed in this paper for the selection of UAV suppliers. First, the 2DLIFV is utilized to represent the uncertain, fuzzy, and linguistic evaluations of the experts on the evaluation attributes. Second, the two-dimension linguistic intuitionistic fuzzy BWM (2DLIF-BWM) is introduced to compute the weights of the attributes. Then, a novel expert weight calculation method that combines the uncertainty degree and consensus degree of the experts is introduced. Next, the 2DLIF-MULTIMOORA method is proposed, where the aggregation operators and distance measures of the 2DLIFVs are used to determine the ranking results of different alternatives. Finally, a real case of selecting a sustainable UAV supplier for power line inspection is presented to illustrate the process of the proposed method. The experimental results are further analyzed through sensitivity and comparative analyses to show the feasibility and effectiveness of the proposed method. From the results, it can be found that the proposed method can more flexibly represent the uncertain assessments while providing reasonable and reliable results.

Exploring the circular economy paradigm: A natural resource-based view on supplier selection criteria

The paradigm of circular economy and the transfer of its principles to supply chain management has recently received much attention from researchers and practitioners. Limited natural resources, governmental legislation, and social responsibility for environmental preservation are the main factors for the development of circular supply chains. Especially in the automotive industry, critical materials are used to produce electric vehicles, reinforcing the importance of circularity for the industry. As the first stage of the supply chain, suppliers have a considerable influence on creating self-sufficient production systems, and in the automotive sector, a low level of vertical integration is important. Therefore, selecting suppliers is an enabler for circular supply chains, but this decision is a complex process due to multiple, partly conflicting criteria. To contribute to the knowledge in this research area, this study applies a fuzzy decision-making trial and evaluation laboratory approach for supplier selection in a circular supply chain in a case study of electric vehicles. First, a set of criteria for supplier selection in circular supply chains was identified based on a literature review and was systematically categorized based on the natural resource-based view. Afterward, the criteria were assessed and refined by experts with a strong procurement background in the automotive industry. Second, the experts were interviewed for value collection, and finally, the mutual influence of the criteria was determined. The results show that the most important criteria for circular supplier selection in electric vehicle supply chains are environmental standards, environmental-related certifications, resource consumption, and waste generation. Regarding the natural resource-based view, short-term focused criteria addressing the capability of pollution prevention have a higher importance compared to long-term criteria that drive sustainable development. In addition, this study classifies the criteria into the categories of causes and effects, discusses the prominence of the criteria, and presents a strategic map showing the mutual influences of the criteria. The results contribute to the theoretical and practical discussion on circular supply chains by identifying the key criteria for circular supplier selection and providing decision-making support for procurement managers.

Multicriteria Approach for Supplier Selection: Evidence from a Case Study in the Fashion Industry

Worldwide manufactures are redesigning supply chains, often outsourcing with the aim of maintaining a competitive advantage and gaining market share. When selecting and purchasing fabrics, firms should actively cooperate with suppliers, to meet customers’ needs. In this view, the supplier selection process plays a key role in keeping a competitive edge in global markets. Therefore, this study proposes a multicriteria decision-making model (MCDM) to ease supplier evaluation and selection. Supply chain operation reference metrics (SCOR metrics) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) are combined to build a model for supplier selection. The former allows us to conduct a very thorough fact-based analysis of all features in the supply chain, while the combination of fuzzy theory and SCOR model allows us to deal with uncertainty. The main novelty of this method is that it incorporates a consolidated supply chain management criteria within the framework of fuzzy set theory and multicriteria decision-making model (MCDM) facilitating their application into practice. The proposed approach is tested by considering the case of a manufacturing firm in the fashion industry willing to designate the most appropriate supplier within a set of three potential ones.

A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry

Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries.

A Novel Enhanced Supplier Selection Method Used for Handling Hesitant Fuzzy Linguistic Information

Today’s competitive businesses have been shifted from the company-to-company competition model to the supply chain-to-supply chain competition model. The selection of the most suitable supplier determines customer satisfaction and enterprise competitive advantage. However, the typical supplier selection approaches did not consider the ordered weights between the evaluations of attribute values, resulting in distorted assessment result. Moreover, experts often uncertainly decide the exact value of the evaluation attribute’s rating, have linguistic term sets equivocation, or give ambiguous information, which increase the difficulty of the supplier evaluation process. To deal with the aforementioned problem, we have proposed a novel enhanced supplier selection method for handling hesitant fuzzy linguistic information. To verify the approach, by taking network security system assessment as an example to explain the use of the proposed novel enhanced supplier selection method, the calculation result is compared with the result of the arithmetic average and symbolic methods. The results show that the proposed novel enhanced supplier selection method is more accurate and reasonable and can better reflect real situations.

A Fuzzy Analysis Approach to Green-Resilient Supplier Selection in Electronic Manufacturing Systems

Adopting environmentally sound practices and utilizing resilient issues to face disruptive events and risks are new important agendas for enterprises, governments, and societies. The issue of supplier selection and evaluation in accordance with green and resilient principles has received little attention in the electronic industry. So, the present study aims to consider both criteria of resilience and greenness in a new supplier selection model. This study presents an integrated decision-making method based on the fuzzy set theory and data envelopment analysis (DEA) approach for supplier selection in a supply chain network in an electronic manufacturing company. The fuzzy set theory was used for analyzing suppliers' indicator weights, the suppliers' performance, and rating them with respect to subcriteria. Then, the obtained data were aggregated to classify and prepare them for use in a version assurance region of the DEA method with one dummy input and three output variables to rank the suppliers. The performance of the proposed DEA-based model has been verified through the sensitivity analysis and it has been validated by the existing FIS-based model in the literature. Furthermore, the proposed DEA-based model can be designed with any number of suppliers and criteria and subcriteria.

An integrated decision-making approach for global supplier selection and order allocation to create an environment-friendly supply chain

PurposeDue to the fierce market competition, many organizations seek global suppliers because of lower procurement costs and better product quality. However, selecting suitable global suppliers is one of the complicated decision-making tasks for decision-makers due to the involvement of various qualitative and quantitative factors. The primary purpose of this research is to design an integrated approach for global supplier selection and order allocation in the context of developing an environment-friendly supply chain under data uncertainty.Design/methodology/approachInitially, the fuzzy analytical hierarchy process (FAHP) is used to calculate the selected criteria weights. After that, the weights obtained from FAHP are inserted into the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to examine the performance of selected suppliers and determine their final ranks. Finally, the obtained results from FTOPSIS are incorporated into the multi-choice goal programming (MCGP) model, which involves multi-aspiration levels to allocate the optimal order quantity to the selected global suppliers.FindingsA real-time case study of the automotive industry is presented to demonstrate the efficiency and practicality of the suggested approach. The case study and sensitivity analysis results show that the proposed model effectively tackles suppliers' evaluation and order allocation data uncertainty.Originality/valueIncorporation of risks, environmental management and economic factors during global supplier selection in the automotive sector has not been given much attention in the past literature. So, this research aims to fulfill the gap by developing an integrated approach that can tackle data uncertainty effectively.

An Integrated Approach for Supplier Evaluation and Selection using the Delphi Method and Analytic Hierarchy Process (AHP): A New Framework

Supplier selection is one of the most critical processes in supply chain management (SCM). Most small and medium enterprises (SMEs) face difficulties choosing the best supplier using conventional methods. A hybrid multi-criteria decision-making (

A Bi-objective AHP-MINLP-GA approach for Flexible Alternative Supplier Selection amid the COVID-19 Pandemic

A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach. First, AHP is applied to decompose the decision maker's judgment matrix into several sub-judgment matrices. Each sub-judgment matrix represents a single viewpoint and generates a priority set. To generate diversified priority sets, a bi-objective MINLP problem is solved using a GA, and multiple alternatives can be selected based on these priority sets. The proposed approach has been applied to the real case of choosing diversified alternative suppliers amid the COVID-19 pandemic to assess its effectiveness. Several existing methods were also applied to this case for comparison. Experimental results showed that only the proposed approach was able to diversify the recommended alternative suppliers that were simultaneously optimal, thereby enhancing decision-making flexibility. In addition, the application of GA increased the solution efficiency by up to 75%.

An integrated approach for a sustainable supplier selection based on Industry 4.0 concept.

The recent advances in sustainable supply chain management are integrated with Industry 4.0 concepts. This study develops a new integrated model to consider the sustainability and Industry 4.0 criteria for the supplier selection management. The proposed approach consists of the fuzzy best worst method (FBWM) and the two-stage fuzzy inference system (FIS) to assess the selection of suppliers. Firstly, this study determines a comprehensive list of Industry 4.0 and sustainability criteria along with their definitions. Then, the importance weight of each criterion is computed by the FBWM. Subsequently, a two-stage FIS is devoted to nominate the suppliers' performance with regard to the sustainability and Industry 4.0 criteria. To show the applicability of our integrated model, a case study for a textile company in Iran is provided. Finally, some sensitivity analyses are done to assess the efficiency of the proposed integrated approach. One finding is to establish a decision-making framework to evaluate suppliers separately, rather than relatively in a fuzzy environment using Industry 4.0 and sustainability criteria.

An integrated group fuzzy best-worst method and combined compromise solution with Bonferroni functions for supplier selection in reverse supply chains

Choosing the right supplier has a significant impact on the efficiency and productivity of a supply chain (SC). Different supplier selection models and approaches have been developed for reverse SCs. The lean, agile, resilient, and green (LARG) strategy is an innovative paradigm for supply chain competitiveness and sustainability. This study proposes a fuzzy-based methodology that integrates the fuzzy group best-worst method (FG-BWM) and the fuzzy combined compromise solution (FCoCoSo) method for supplier selection in reverse SCs within a LARG strategic paradigm. The FG-BWM is used to measure the importance weights of the supplier selection criteria. Subsequently, FCoCoSo is coupled with the normalized weighted geometric Bonferroni mean functions to select the most suitable supplier. The BWM is used for its simplicity and efficacy. The CoCoSo model is used for its unique ability to produce a compromise solution. The Bonferroni functions are used to capture the interrelationships among the decision attributes and eliminate the influence of extreme data. The main proposed framework aims at providing an easy-to-implement but reliable method to help manufacturers active in recycling and concerned with sustainability issues to rank and select suppliers. We present a real-world case study whose results demonstrate the applicability of the proposed integrated framework to supplier selection in reverse SCs, focusing in particular on the wood and paper industry.

Small and Medium Enterprises' Supplier Selection Approaches

Undoubtfully, successful performance of small and medium-sized enterprises (SMEs) greatly depends on the ability of their management to integrate the complex network of business relationships in selecting suppliers. The purpose of this paper is to explore the supplier selection approaches of small and medium enterprises (SMEs). The main approaches followed are exploratory- and descriptive research aimed at exploring and describing how SMEs choose their suppliers. The most favoured criterion (factor) for supplier selection was flexibility, with delivery being the second most preferred followed by the financial status and capabilities and motivation of the supplier. The failure rate for small businesses is high and the results of this study could advise small businesses which criteria to pay attention to, to prevent business failure. As competition is fierce in the SME sector, it is important to choose suppliers that meet ISO 14000 certification in addition to ISO 9000 quality certification, as it shows customers that these SMEs offer quality products and are environ-mentally conscious.

Machine learning and optimization models for supplier selection and order allocation planning

Supplier selection and order allocation have significant roles in supply chain management. These processes become major challenges when the demand is uncertain. This research presents a new two-stage solution approach for supplier selection and order allocation planning where a forecasting procedure is integrated with an optimization model. In the first stage, the demand is forecasted to handle the demand vagueness. A novel Relational Regressor Chain method is introduced to determine the future demand, which is compared with the Holt's Linear Trend and the Auto-Regressive Integrated Moving Average methods to ensure the forecasting accuracy. The forecasted demand is then fed to the second stage where a multi-objective programming model is developed to identify suitable suppliers and order quantities from each supplier. Weighted-sum and ε-constraint methods are utilized to obtain the efficient solutions. To our knowledge, this paper is the first study that has integrated demand forecasting with the supplier selection and order allocation planning. A real dataset from a Canadian food supply network is used to examine the results of the forecasting methods and to determine the orders allocated to each supplier. The results of the forecasting methods show that the proposed Relational Regressor Chain method can forecast demand with a higher precision than the other forecasting methods considered in this paper. It is also evident from the results that the selection of the forecasting methods may have impact on both the selection of suppliers and the orders allocated to them.

An integrated decision-making approach for sustainable supplier selection in the chemical industry

Due to an increased awareness of ecological protection and resultant stringent legislations, business organizations are highly motivated to improve the sustainable performance of their supply chain in order to achieve sustainable development goals. The chemical industry is a high-risk, high-pollution, and high-efficiency industry, that would benefit from a systematic and sustainability focused evaluation system for supplier selection. Yet, to date, few studies have conducted the necessary in-depth analysis of the characteristics of this industry from the economic, social, and environmental perspectives. Despite the many methods and models that have been proposed to resolve the sustainable supplier selection (SSS) problem, no research has yet considered the different characteristics of each triple bottom line dimension. Accordingly, this paper addresses this problem by proposing a hybrid multi-method and multi-criteria decision-making framework for SSS in the chemical industry. Based on specific characteristics of the chemical industry, this study applies Fuzzy Grey Relational Analysis (FGRA), Failure Mode and Effects Analysis (FMEA), and cloud computing-entropy weight method (EWM) to analyze the economic, social, and environmental dimensions, respectively. Finally, this study integrates the evaluation results of the three dimensions using the Decision-making Trial and Evaluation Laboratory (DEMATEL). The proposed approach and decision-making model can help managers of sustainable supply chains in the chemical industry to choose more sustainable suppliers, respond to market demands quickly, and maintain high competitiveness in the market. An illustrative application of the proposed framework and model is undertaken in one of the biggest Chinese petrochemical companies to verify its practicality and reliability.

An Integrated Decision-Making Approach for Green Supplier Selection in an Agri-Food Supply Chain: Threshold of Robustness Worthiness

Along with the increased competition in production and service areas, many organizations attempt to provide their products at a lower price and higher quality. On the other hand, consideration of environmental criteria in the conventional supplier selection methodologies is required for companies trying to promote green supply chain management (GSCM). In this regard, a multi-criteria decision-making (MCDM) technique based on analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is used to evaluate and rate the suppliers. Then, considering the resource constraint, weight of criteria and a rank of suppliers are taken into account in a multi-objective mixed-integer linear programming (MOMILP) to determine the optimum order quantity of each supplier under uncertain conditions. To deal with the uncertain multi-objectiveness of the proposed model, a robust goal programming (RGP) approach based on Shannon entropy is applied. The offered methodology is applied to a real case study from a green service food manufacturing company in Iran in order to verify its applicability with a sensitivity analysis performed on different uncertainty levels. Furthermore, the threshold of robustness worthiness (TRW) is studied by applying different budgets of uncertainty for the green service food manufacturing company. Finally, a discussion and conclusion on the applicability of the methodology is provided, and an outlook to future research projects is given.