Decision-Making Models for solving Green Supplier Evaluation and Selection problems

For any industry to improve and expand, the proper evaluation and selection of suppliers is essential. In order to establish whether a supplier is appropriate for working with a company, a system for selecting green suppliers is required. A variety of Decision-Making (DM) models have been created by researchers to address the problems associated with evaluating and choosing green suppliers. In order to address the Green Supplier Evaluation and Selection (GSES) challenge, we did a thorough investigation of ten works of literature, in order to find out which approach is the most widely used and which is more efficient. This study primarily focuses on the findings of ten reviews that examined 1098 research publications from academic journals between 1990 and 2020. 271 DM models examined that were broken down into 170 individual models and 101 combination models, our analysis only looked at the single models. The method of Analytic Hierarchy Process (AHP) is the dominant model used by 160 articles, 122 studies used Data Envelopment Analysis (DEA), and finally 101 research works that utilized the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model. In addition, we found that the biggest percentage “62%” of studied articles used multi-criteria decision-making (MCDM) models. As a result, the most widely utilized Decision Making models to address the evaluation and selection of green supplier were found to be AHP, DEA, and TOPSIS.

With the increasing environmental awareness, apparel manufacturers have begun to consider environmental issues in supplier evaluation and selection. It is crucial to assess suppliers based on their environmental performance along with other criteria for supplier selection. This paper addresses the green supplier evaluation and selection problem in global apparel manufacturing by developing a methodological framework for green supplier evaluation and selection based on the triple bottom line principle and a fuzzy multi-criteria decision-making (MCDM) model. First, a green supplier evaluation criteria hierarchy based on the triple bottom line principle is established based on comprehensive literature review, on-site investigation and policy analysis. Then, a fuzzy MCDM model is presented to evaluate and select the best material supplier. Finally, a sensitivity analysis is conducted to verify the effectiveness of the proposed framework. Results show that the proposed framework can handle green supplier evaluation and selection in apparel manufacturing effectively.

On the background of global warming, energy shortages and environmental pollution, people pay more attention to the low-carbon economy.In this paper, the problem of green supplier selection based on low-carbon economy is studied.Concretely, an evaluation index system for green supplier selection under the low-carbon economy is presented, and then a multi-attribute decision model of green supplier selection is established based on the method of analytic hierarchy process (AHP).This model provides decision reference for the department of supply chain management.

The incorporation of environmental criteria into the conventional supplier selection practices is essential for organizations seeking to promote green supply chain management. Challenges associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. The development and implementation of practical decision making tools that seek to address these challenges are rapidly evolving. This article contributes to this knowledge area by comparing the application of intuitionistic multi-criteria supplier selection methods in a fuzzy environment. The intuitionistic fuzzy AHP and TOPSIS methods are thoroughly discussed. The methods are then utilized to complete a green supplier evaluation and selection study for an actual company from the paper mill. For this purpose, 3 alternative green suppliers have been identified. To evaluate the alternatives, 10 criteria in three dimensions including supplier performance, environmental protection and supplier risk have been selected. Since the Multi-Criteria Decision Making (MCDM) methods are often used in problem of supplier selection from past to present. The Intuitionistic Fuzzy Sets (IFS) are used to reflect approval, rejection, and hesitation of decision makers by dealing with real life uncertainty, imprecision, vagueness, and linguistic human decisions. The main purpose of this study is to define the weights of criteria with Intuitionistic Fuzzy AHP. After that, the Intuitionistic Fuzzy TOPSIS and AHP method were used to rank alternatives from best to worst. At the end of the study the results related with alternatives were compared and interpreted.

Supplier evaluation and selection is an important issue in the supply chain management. Many different factors would have effect on the performance of suppliers, therefore supplier evaluation and selection is a typical multi-criteria decision making problem. Data Envelopment Analysis (DEA) is an effective methodology of evaluating the relative efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. DEA has been widely used in the supplier evaluation and selection problem. However, traditional literatures mostly concentrated on the supplier evaluation and selection without consideration of the decision maker's preference. In this paper, we propose a weight restricted DEA model for the supplier evaluation and selection problem in which two kinds of decision maker's preferences can be reflected by introducing different kinds of weight restrictions. Numerical example is provided to examine the validity and effectiveness of our proposed model. KEYWORD: data envelopment analysis; supplier evaluation; supplier selection; weight restriction

Green supply chain management is concerned with the integration of environmental criteria and sustainability issues in the management of the supply chain. Within this framework, firms applying internally green strategy have, naturally, the interest to pressure purchases of goods and services from suppliers that are themselves green, at least to a certain extent. Thus, Supplier Evaluation and Selection is crucial due to its big impact on business function. The prospect of applying green principles has consequently become an important feature of a supplier’s overall performance. Hence, green supplier selection and evaluation, although a relatively new research subject, has grown quite rapidly. It develops and studies the decision and evaluation models based on environmental criteria. Its main tools are based on multi-criteria decision making approaches. Our objective, in this paper, is to review journal articles published in the period 2012–2019 on this topic, in order to identify the most widely applied approaches for green supplier evaluation and selection and the most cited green criteria.

PurposeSupply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk.Design/methodology/approachAt primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics.FindingsA real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model.Practical implicationsThe proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors.Originality/valueA new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.

Supplier selection is one of the most important activities of a company. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer's company. In addition, green production has become an important issue for almost every manufacturer and will determine the sustainability of a manufacturer. Therefore a performance evaluation system for supplier and green suppliers is necessary to determine the suitability of suppliers to cooperate with the company. Supplier and green supplier selection is a multiple criteria decision making problem in which the objectives are not equally important. In practice, vagueness and imprecision of the goals, constraints and parameters in these problems make the decision making complicated. The objective of this study is to construct a decision-making process using fuzzy analytic hierarchy process (FAHP) and balanced scorecard (BSC) for evaluating supplier and green suppliers in the manufacturing industry. The BSC concept is applied to define the hierarchy with four major perspectives and performance indicators are selected for each perspective. FAHP is then proposed in order to tolerate vagueness and ambiguity of information. Finally, FAHP is applied to facilitate the solving process. With the proposed approach, manufacturers can have a better understanding of the capabilities that supplier and green supplier must possess and can evaluate and select the most suitable supplier for cooperation.

Supplier selection problem has a major regard in terms of the performance of supply chain of an organization. Several various approaches were proposed, including the analytic hierarchy process, fuzzy analytic hierarchy process, and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS). However, no comparative researches of these three approaches related to the supplier selection problem have been carried out. Therefore, this article proposes a methodology to evaluate the selected approaches. The evaluation was conducted based on the following factors: agility during the decision process, computational complexity, number of criteria and alternative suppliers, and adequacy in supporting a group decision. The methodology is implemented in X company. The results show that each approach is convenient to the supplier evaluation and selection problem, particularly toward the support of group decision-making and uncertainty modeling. In terms of computational complexity, analytic hierarchy process performs better than fuzzy TOPSIS and fuzzy analytic hierarchy process. Moreover, the fuzzy TOPSIS approach is better suited to the supplier evaluation and selection in terms of agility during the decision process, the number of criteria and alternative suppliers, and the adequacy in supporting a group decision.

The green supply chain operations try to minimize environmental impact over the product's lifetime including product recycling or use, reduction of harmful substances, resource saving, green design, etc. Supplier selection is the vital issue in green purchasing. This paper aims to develop applicable and efficient methodology for green supplier selection. The proposed methodology includes the combination of Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA-F) and Fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS-F) methods. Fuzzy extensions of these methods are preferred because of the complexity of the green supplier selection problem and inclusion of both quantitative and qualitative criteria. Also, these criteria may be uncertain and conflict with each other. It is the first time that SWARA-F is combined with MARCOS-F for the green supplier assessment and selection of the best one among them. The effectiveness of the proposed methodology is demonstrated by solving the real selection problem of a company from textile industry. In the problem both classic and green criteria including main and sub-criteria are considered. SWARA-F is used for weighting the evaluation criteria and the rank of each green supplier alternatives is obtained from incomplete information by assessment score calculated from MARCOS-F. The effectiveness of the combination of two methods is verified by sensitivity and comparative analyses. The proposed methodology provides acceptable and satisfactory results in determining the best green supplier namely improving the environmental and cost efficiency evaluation process.

An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment

With the vigorous development of the global economy, modern enterprises have agreed to implement green supply chain management. The evaluation and selection of green suppliers plays a vital role in successful green supply chain management. As a multi-attribute group decision-making green supplier selection problem, it is difficult to fully describe the evaluation factors using precise numerical values and classical fuzzy sets, so it cannot assist enterprise managers in making completely reasonable decisions. Therefore, this paper proposes a green supplier evaluation and selection method based on interval BPA-based intuitionistic fuzzy sets. First, build a green supplier evaluation index system based on the characteristics of the papermaking enterprise's suppliers. Then, the intuitionistic fuzzy number is used to express the indicators of the evaluator for each supplier, and the intuitionistic fuzzy information is integrated through the organic combination of the IIFWA integration operator and the structural entropy weight to obtain the comprehensive evaluation value of the evaluator. Finally, the interval BPA-based intuitionistic fuzzy set method ranks the candidate suppliers and determines the best green supplier. Applying this method to the field of green supplier evaluation and selection can provide an effective method for enterprises to select suppliers scientifically and reasonably, which has practical reference value and practical significance.

In selecting suppliers, one approach that can be used to solve this problem is Multi-Criteria Decision Making (MCDM). MCDM involves several criteria with both quantitative and qualitative assessments. This research attempts to solve the Green Supplier Selection (GSS) problem by using the integration of Analytical Hierarchy Process (AHP) and Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA). AHP is used as the weighting of the GSS criteria and sub-criteria. Furthermore, MOORA is used to rank the best suppliers. This research was conducted in the textile industry in Indonesia. Eight criteria and 15 sub-criteria GSS are used to select eight suppliers. The results show that the product price has the most excellent weight in supplier selection. In addition, the AHP-MOORA method can be used in solving GSS problems.

Supplier evaluation and selection problems are inherently multi-criteria decision problems. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems. Data Envelopment Analysis (DEA) has been used to evaluate suppliers' performance when there are multiple inputs and outputs in the supplier selection problem. The DEA determines the relative efficiencies of multiple suppliers. These relative efficiencies are then used to provide benchmarking data for reducing the number of suppliers. The DEA models used for supplier selection require numerical data for all the inputs and outputs for all the suppliers. However, this information may not be readily available in real-world problems. In this paper, we propose a novel DEA model that addresses this gap in the supplier evaluation literature. The proposed model can measure suppliers' efficiency in problems exhibiting: the presence of undesirable outputs; the lack of input variables and the presence of zero or negative values in the data set. We also present a case study at Saipa, Iran's second-largest car maker, to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms. 2012 Inderscience Enterprises Ltd.

<abstract><p>The green chain supplier selection process plays a major role in the environmental decision for the efficient and effective supply chain management. Therefore, the aim of this paper is to develop a mechanism for decision making on green chain supplier problem. First, we define the Hamacher operational law for Pythagorean cubic fuzzy numbers (PCFNs) and study their fundamental properties. Based on the Hamacher operation law of PCFNs, we defined Pythagorean cubic fuzzy aggregation operators by using Hamacher t-norm and t-conorm. Further, we develop a series of Pythagorean cubic fuzzy Hamacher weighted averaging (PCFHWA), Pythagorean cubic fuzzy Hamacher order weighted averaging (PCFHOWA) Pythagorean Cubic fuzzy Hamacher hybrid averaging (PCFHHA), Pythagorean Cubic fuzzy Hamacher weighted Geometric (PCFHWG), Pythagorean Cubic fuzzy Hamacher order weighted Geometric (PCFHOWG), and Pythagorean Cubic fuzzy Hamacher hybrid geometric (PCFHHA) operators. Furthermore, we apply these aggregation operators of Pythagorean Cubic fuzzy numbers to the decision making problem for green supplier selection. We construct an algorithm for the group decision making by using aggregation operators and score function. The proposed decision making method applies to green chain supplier selection problem and find the best green supplier for green supply chain management. The proposed method compared with other group decision techniques under Pythagorean cubic fuzzy information. From the comparison and sensitivity analysis, we concluded that our proposed method is more generalized and effective method.</p></abstract>