Supplier Selection with Fuzzy Analytical Hierarchy Process

Supply chain has a very extensive and dynamic structure that incorporates new business models, new customer expectations, market searches and technological developments. With the introduction of Industry 4.0 into supply chain, a rapid and intensive process of digitalization begins to transform every step of supply chain. Supply chain selection is one of the essential decisions in reducing the supply chain cost and improving overall quality of product and services. With the implication of digital technologies and Industry 4.0 on supply chain, the supplier selection process has been significantly changed during the recent years. Companies are willing to need new requirements for their own suppliers in accordance with Industry 4.0 implementations and technologies. This paper aims to identify key criteria to Industry 4.0 technologies and evaluate them to select the right suppliers selection in the era of Industry 4.0. Within the scope of this study attempts to develop an integrated interval type 2 fuzzy AHP and GOPRAS-G methodology to select the appropriate supplier in the face of Industry 4.0 implementations. For this purpose, interval type 2 fuzzy AHP was employed to weight the supplier evaluation criteria and then, Gray COPRAS method has been applied to prioritize suppliers. This paper is to provide practitioners and researchers with insight into how Industry 4.0 strategies influence on supplier selection.

Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) model (economic, environmental, and social aspects) and literature reviews; in addition, the fuzzy analytic hierarchy process (FAHP) method was utilized to identify the weight of all criteria in the second stage. The technique for order preference by similarity to an ideal solution (TOPSIS) is a multicriteria decision analysis method, which is used for ranking potential suppliers in the final stage. As a result, decision-making unit 10 (DMU/10) is found to be the best supplier for the garment industry. The contribution of this research includes modeling the supplier selection decision problem based on the TBL concept. The proposed model also addresses different complex problems in supplier selection, is a flexible design model for considering the evaluation criteria, and is applicable to supplier selection in other industries.

Steel is one of the most powerful industries globally, and the associated products have a tremendous impact on nurturing a sustainable society. Considering environmental concerns within this industry's supply chain is highly successful in saving both energy and natural resources and lowering greenhouse gas emissions. In light of this, sustainable suppliers are considered input partners who play a specific role in the chain of business operations of every enterprise, maintaining them to achieve higher levels of customer satisfaction and thus gain more market share. Supplier selection can be characterized as a multi-criteria decision-making (MCDM) problem under a vague and uncertain environment. In this paper, a hybrid model of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) is suggested to determine the most potent green supplier of steel manufacturing in Vietnam based on a complete set of five main criteria (price, quality, delivery, service, environment) through literature review and experts' responses. The significance of each criterion is measured by experts' judgments in linguistic terms, which can be expressed in triangular fuzzy numbers using the FAHP model. Then, the FTOPIS model is deployed to rank alternatives. A case study of Vietnam's top 10 steel manufacturers is implemented to exhibit the model's effectiveness. From FAHP findings, "lead time", "warranty", "defect rate", "supply capacity", and "product price" were recognized as the most impactful criteria. As for the ranking of sustainable suppliers, "Hoa Phat Group Joint-stock company", "Hoa Sen Group", and "Pomina Steel Corporation" are the top three optimal suppliers. In dealing with qualitative data and input uncertainties in the supplier evaluation and selection problem, this paper can suggest more possible solutions and provide significant materials in similar outsourcing selection problems and applications of relevant industries.

Circular supplier selection in the construction industry: A sustainability perspective for the emerging economies

The selection of appropriate suppliers in an uncertain environment influences the sustainability and the competitive advantage of the automotive industry and hence presents one of the significant management problems. In the literature, it is suggested that an acceptable supplier may be determined concerning many criteria. In this manuscript, criteria selection is based on the relevant literature. Handling of different uncertainties is performed by using the type-2 fuzzy sets that have the capability of handling more considered impreciseness and uncertainties. In the presented research, the two-state model is proposed. In the first stage, the relative importance of criteria and sub-criteria are described by pre-defined linguistic expressions which are modeled by the interval type-2 triangular fuzzy numbers (IT2TFNs). The fuzzy weights vectors are given by using the fuzzy Analytic Hierarchical Process with IT2TFNs (IT2FAHP). After that, the ranking of suppliers is based on the modified Multi-Attributive Border Approximation area Comparison method (MABAC) with IT2TFNs (IT2FMABAC). The extension of the conventional MABAC includes: (a) modeling of sub-criteria values of IT2TFNs, (b) the fuzzy criteria values are calculated by using fuzzy algebra rules, (c) belonging of each supplier to border approximation areas (BAAs) is given by using procedure, and (d) the distance of suppliers to BAAs is determined by using the normalized Euclidean distance formulas. The proposed model is tested on the real-life data from the automotive supply chain. Through the presented research, it is shown that the proposed IT2FMABAC is a useful and reliable tool for the rational purchasing decision-making process.

This article outlines a hybrid method, incorporating multiple techniques into an evaluation process, in order to select competitive suppliers in a supply chain. The Fuzzy Analytic Hierarchy Process (FAHP) model is constructed to calculate the weight of various factors, and get comprehensive character value. The Data Envelopment Analysis (DEA) is present to decide the backup suppliers. By performing Axiomatic Fuzzy Set (AFS) clustering method (X. D. Liu et al. IEEE Transaction on Systems, Man, Cybernetics, 2005), the final suppliers are selected and evaluated. In this paper, quantitative and qualitative factors, such as technical capability, product appearance, JIT capability, price, delivery rate and sale service rate are considered to determine suitable suppliers. Numerical example that fifteen suppliers and six criteria are studied and the experimental results show that the proposed evaluation framework is very well suited as supplier selection decisions even with the inter-correlation criteria/attributes.

Today, business organizations are facing increasing pressure from a variety of sources to operate using sustainable processes. Thus, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. To achieve these goals, companies must focus on criteria that include CO2 (carbon footprint) and toxic emissions, energy use and efficiency, wastage generations, and worker health and safety. As in other industries, the food processing industry requires large inputs of resources, which results in several negative environmental effects; thus, decision-makers have to evaluate qualitative and quantitative factors. This work identifies the best supplier for edible oil production in the small and medium enterprise (SME) food processing industry in Vietnam. This study also processes a hybrid multicriteria decision-making (MCDM) model using a fuzzy analytical hierarchy process (FAHP) and green data envelopment analysis (GDEA) model to identify the weight of all criteria of a supplier’s selection process based on opinions from company procurement experts. Subsequently, GDEA is applied to rank all potential supplier lists. The primary objective of this work is to present a novel approach which integrates FAHP and DEA for supplier selection and also consider the green issue in edible oil production in uncertain environments. The aim of this research is also to provide a useful guideline for supplier selection based on qualitative and quantitative factors to improve the efficiency of supplier selection in the food industry and other industries. The results reveal that Decision-Making Unit 1 (DMU 1), DMU 3, DMU 7, and DMU 9 are identified as extremely efficient for five DEA models, which are the optimal suppliers for edible oil production. The contributions of this research include a proposed MCDM model using a hybrid FAHP and GDEA model for supplier selection in the SME food processing industry under a fuzzy environment conditions in Vietnam. This research also is part of an evolution of a new hybrid model that is flexible and practical for decision-makers. In addition, the research also provides a useful guideline in supplier selection in the food processing industry and a guideline for supplier selection in other industries.

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.

Awareness of environmental protection and sustainability in the manufacturing industry has grown, making the green image a more critical factor in the supplier selection process. Recent environmental laws and regulatory reforms have prompted several manufacturing firms to revise their standardized supplier selection criteria. Supplier evaluation and selection are well studied in literature; however, studies with a green focus are relatively limited. To fill this gap, this paper proposes a green supplier evaluation and selection (GSES) method that evaluates suppliers according to their green competencies and environmental performances. In this regard, a hybrid multi-criteria decision making (MCDM) approach capable of handling imprecise quantitative and qualitative data is proposed. To demonstrate the functionality of the approach, a case study is conducted on a US-based company that manufactures and distributes plastic closures and dispensing systems internationally. The results of the algorithm, along with the discussion, are also provided.

Global competition is an unpreventable fact and customer demands are diversified in today's business environment. The result is progressively increased costs and sharply decreased profit. In consequence, supplier selection has become one of the critical tasks to ensure the profitability and survival of a company. The supplier selection problem is multi-criteria in nature, and a multi-criteria decision making method is necessary to solve such as problem. In this paper, a supplier performance evaluation model based on fuzzy analytic hierarchy process (FAHP) and data envelopment analysis (DEA) is constructed. DEA is applied first to evaluate quantitative factors, and the results are transformed into pairwise comparison values for FAHP analysis. With the consultation with the experts, qualitative factors are evaluated through FAHP analysis, so are the relative importance of factors. A final ranking of suppliers can be obtained by combining the quantitative and qualitative results. The priorities of the factors and the ranking of the suppliers can be a recommendation or reference for decision makers in making a supplier evaluation and selection decision.

A decision-making process requires a prior definition and fulfilment of certain factors, especially when it refers to complex fields such as supply chain management. One of the most important items in the initial stage of a supply chain, which strongly influences its further flow, is making a decision on the most suitable supplier. In this paper, a model for evaluation and supplier selection has been proposed, which has been considered in more than ten different production areas. The model consists of twenty quantitative and qualitative criteria, which are reduced to a total of nine by the application of the fuzzy AHP and the assessment of managers in production companies. The verification of the model has been presented throughout a selection of suppliers in a company for the production of plastic bags and foils, where the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method has been used to determine the significance of the criteria, and the Fuzzy Evaluation based on Distance from Average Solution (Fuzzy EDAS) to evaluate and select suppliers. The obtained results have been considered throughout a sensitivity analysis in which a total of 15 different scenarios have been formed and where the stability of the model has been determined, since the supplier one is the best solution in all the cases.

Comprehensively examining costs and benefits in supplier selection makes the right choice challenging Globalization increases alternatives, yet selecting the most appropriate remains difficult. Therefore, the use of scientific methods is critical when choosing among alternatives. Such methods not only facilitate the decision-making process but also enable more accurate and reliable outcomes. In the public sector, outsourcing supplier selection is typically conducted through a tender-based process, where cost is primary; however, relying solely on it can lead to suboptimal decisions. Public procurement processes proceed by identifying needs, making announcements, and inviting companies to participate in the tender. Multi-Criteria Decision-Making (MCDM) methods are commonly used tools for addressing complex problems with multiple factors, such as supplier selection. MCDM methods allow decision makers to evaluate each criterion for all alternatives and determine the option that best meets the desired requirements. Furthermore, techniques such as fuzzy logic within MCDM methods go beyond numerical data and enable decision makers to evaluate the criteria and alternatives with more understandable linguistic expressions. Although supplier selection problems are frequently examined using Fuzzy AHP and Fuzzy TOPSIS methods, studies specifically focusing on the public sector remain limited. This study addresses the problem of selecting a cleaning company. It integrates Fuzzy AHP and Fuzzy TOPSIS, aiming first to determine criteria weights using Fuzzy AHP and then to identify the most suitable supplier via Fuzzy TOPSIS. The study was implemented as an alternative method to tender-based outsourcing company selection in the public sector and is deemed beneficial.

The raw material is an essential aspect of ensuring smooth production. The selection of raw material suppliers needs to be done to sustain the company's production and development. Wisarka company is an export-oriented furniture company. It uses water hyacinth (Eichhornia crassipes) as the primary raw material. The company is required to maintain and improve performance. The company needs to select the right supplier. This study uses the Multi-Criteria Decision Making (MCDM) the Fuzzy Analytical Hierarchy Process (FAHP) method. It was used for selecting suppliers with criteria like price, quality, flexibility, delivery, warranty, and service. The result of the study show FAHP effective in selecting suppliers.

Global supplier selection is a multi-goal multi-criteria problem which needs to consider both qualitative and quantitative factors. Which suppliers are the best and how much should be purchased from the selected suppliers is an important purchasing issue for manufacturers. Traditionally, decision makers can determine the best supplier from evaluating few suppliers with qualitative supplier selection criteria by using fuzzy analytic hierarchy process (FAHP), but evaluate dozens of global suppliers simultaneously or determine the order quantity from them will be complex and difficult. Meanwhile, decision makers can determine the order quantity form the suitable suppliers by using fuzzy goal programming (FGP); however, it is not easy to decide weights for each goal of global supplier selection with different supply chain strategies. This study integrated the FAHP and FGP (FAHP-FGP) method to be a new approach for global supplier selection in considering the manufacturer’s supply chain strategies. With FAHP-FGP method, the manufacturer can consistently integrate multi-manager’ opinions in determining weights of each goal and obtain the order quantities for suitable suppliers based on manufacturer’s strategies. To demonstrate the usefulness of the proposed method, a real-world case of a digital consumer products manufacturer is presented.

Nowadays, shareholders are being encouraged to enhance supply chain risk management in order to deal with several disruptive and operational risks. Producing spice and flavor is one of the high-risk industries as the main raw material is spice and its quality is variable and strongly depends on suppliers, seeds, and climate. Having a constant quality of spices and producing a flavor that satisfies every customer’s demand is intricate. Therefore, these companies encountered a variety of goods which increased complexity and uncertainty in supply, processing time, and demand. To come up with risks and uncertainties, in this paper a robust bi-objective multi-product mathematical model is developed to consider resiliency and uncertainty in a multi-period, multi-item supply chain network design simultaneously. The first objective function maximizes total profits when the second one maximizes the total score of resiliency in which Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) have been used to obtain the four resiliency factors weights for the objective function. We proposed an approach for making coordination between production planning, distribution, supplier selection, and order allocation in a flavor plant. The -constraint method is used to obtain Pareto solutions for a real case in Iran. Finally, sensitivity analysis has been done for tracking the trade-off between optimization and robustness with different robust parameters and investigating unmet demand and benefits of resilience consideration.