Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach.
Low-carbon supply chain has been a popular research topic in recent years due to increasing environment stress on economy and global warming, which are mainly caused by carbon emissions. Trucost [
Although many studies exist on the topic of supplier selection, the research on the green supplier selection in low-carbon supply chain is fairly rare [
Based on the above discussion, the objective of this research is to develop an integrated approach for green supplier selection in low-carbon supply chain. Considering the economic, environmental, and social aspects in the low-carbon supply chain condition, we establish 4 main criteria and 22 subcriteria for green supplier selection. An integrated method based on fuzzy set theory and grey relational analysis for green supplier selection is proposed. The proposed methodology integrates the merits of fuzzy set theory and grey relational analysis. Fuzzy set theory is used for the nature of unquantifiable and incomplete information, and judgments are fuzzy in green supplier selection. Grey relational analysis is used to calculate the weight of each criterion due to the grey state of people’s understanding of the weights of criteria for green supplier selection. The strength of the proposed method is that, despite the vagueness of experts’ opinions in the selection process, the model is easy to apply. Moreover, with the proposed method, enterprises can help their suppliers to improve sustainability for better management of low-carbon supply chain operations. A real case in steel industry is also studied to verify the applicability of the proposed criteria and methods for green supplier selection in low-carbon supply chain.
The main contribution of this paper is developing a fuzzy-grey MCDM approach for green supplier selection in low-carbon supply chain. The paper establishes the main criteria and subcriteria for green supplier selection after considering enterprises’ requirements in low-carbon supply chain management, which can help enterprise to identify the potential areas where green suppliers need to improve. The proposed method provides a mechanism of integrating the economic, social, and environmental criteria to fully reflect the requirements of low-carbon supply chain, which helps to avoid potential risk of selecting the wrong suppliers. In the proposed method, it also introduces a membership function of normal distribution, which is a dimensionless method. To the best of our knowledge, no previous studies have investigated the subject of green supplier selection with this kind of integrated method. The proposed method has been successfully implemented in a case company to select its best green supplier and analyze its most appropriate alternative green supplier. This research’s results will improve the managers’ view on the nature of green supplier selection criteria. Besides, the proposed method can be widely used as a structural model for green supplier selection.
The rest of this paper is organized as follows: Section
Supplier selection has received considerable attention for its significant effect towards successful supply chain management. A considerable number of literature reviews are accomplished by various authors such as Govindan et al. [
Climate change is global in scope and it is necessary to reduce carbon emissions at the global level. Since carbon emissions are omitted across the entire supply chain, the focus of a firm’s environmental management to reduce carbon emissions has shifted from individual firms towards the entire supply chain. The objective of low-carbon supply chain is to reduce carbon emissions across the entire supply chain.
Shaw et al. [
These literatures show low-carbon supply chain has become a popular research topic. Due to the characteristics of carbon emissions, it is important for the enterprises to form a low-carbon supply chain to reduce carbon emissions, in which selecting suitable green supplier is one of the key factors. The criteria and methods for green supplier selection are two extremely important aspects, which determine whether the enterprise can achieve the goal of low-carbon supply chain management or not to a certain extent.
Many authors have stressed the importance of selecting suitable (qualitative and quantitative) criteria in the green supplier selection process. The traditional criteria for supplier selection have solely considered economic aspects for many years [
Lee et al. [
Most of the above literatures focus on economic supplier selection, while neglecting environmental and social aspects. It is inappropriate in the era of low-carbon economy, especially in low-carbon supply chain condition. The criteria determine what we should consider for green supplier selection, so they should adhere to scientific, dynamic, comprehensive, and oriented principles. That is to say, the criteria should include economic, environmental, and social aspects, which can meet the enterprise’s requirements for green supplier selection in low-carbon supply chain.
Extensive MCDM methods have been proposed for supplier selection, like the analytic hierarchy process (AHP), analytic network process (ANP), data envelopment analysis (DEA), fuzzy set theory, genetic algorithm (GA), mathematical programming, technique order preference by similarity to ideal solution (TOPSIS), and so forth and their hybrids [
Amindoust et al. [
These methods provide a good research basis for this paper. Due to the fuzziness of judgment, fuzzy set theory is always used in supplier selection, such as literatures [
From the above literature review, although these proposed criteria and methods have brought great insights to supplier selection, some problems also need to be discussed. The criteria for green supplier selection should include economic, environmental, and social aspects, meeting with the requirements of enterprise in low-carbon supply chain. Due to the nature of unquantifiable and incomplete information, we can use fuzzy set theory to select green supplier. The membership function of normal distribution is introduced in this paper, which does not need to nondimensionalize the criteria and can deal with the qualitative criteria. In addition, because people’s understanding of the weights of criteria for green supplier selection is in a grey state, we use grey relational analysis to calculate the weights of criteria. Therefore, we propose a fuzzy-grey multicriteria decision making approach for green supplier selection in low-carbon supply chain.
According to these reviews and the identified criteria mentioned above, especially with the help of the managers and practitioners in low-carbon supply chain management, we establish 4 main criteria (enterprise low-carbon qualification, low-carbon production and service, low-carbon business operation, and low-carbon innovation) and 22 subcriteria (e.g., low-carbon image and green and low-carbon certifications) for green supplier selection. See Table
Criteria for green supplier selection in low-carbon supply chain.
Main criteria | Subcriteria | |
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Green supplier selection in low-carbon supply chain |
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In the complex and changing market environment, if enterprises want to maintain the long-term stability of low-carbon supply chain competition, they should choose those suppliers who have advanced management and good prospects for development in supply chain, thus providing a reliable protection for low-carbon investment and transformation. Finally, they also need to consider the business desire and credibility of cooperation to reduce carbon emissions, which is a prerequisite to establish low-carbon strategic cooperation between enterprise and its supplier.
We can measure enterprise low-carbon qualification from these aspects: the current business status of the enterprise and low-carbon ability for future development and cooperation. The current business status of the enterprise includes enterprise scale, profitability, and debt level. The low-carbon ability for future development includes low-carbon development potential and R&D innovation. The low-carbon cooperation ability includes enterprise reputation and desire of low-carbon cooperation.
On one hand, the competitiveness of low-carbon supply chain depends on rapid response to the market, providing customers with high quality and inexpensive products or service to meet their requirements and expectations, which is the same as the traditional supply chain. On the other hand, it also needs to improve service quality and user experience, strengthen after-sales service support, consolidate supply chain cooperation to reduce unnecessary consumption of all aspects, and control carbon emissions. We can measure low-carbon production and service from four aspects: quality, price service, guarantee, and compensation.
High level of low-carbon supply chain business operation is not only the basis to achieve customers value goal but also the guarantee to reduce carbon emissions which can be reflected by the low-carbon production planning, low-carbon information sharing, cost control, and so forth in the supply chain management. Therefore, green supplier selection in low-carbon supply chain should focus on supplier’s flexibility, efficiency, information, and other aspects of performance.
Transportation is one of the important energy consumption methods in the low-carbon supply chain business operation. Under the same condition, the enterprise should choose the supplier that has the lower transportation cost for the purpose of low-carbon transportation. In addition, the supplier’s strategic compatibility should also be taken into account. Because the strategic objectives indicate the future development of the enterprise, the conflict of strategic objectives will lead to the failure of carbon reduction cooperation. Here, we use low-carbon production flexibility, transportation, strategic compatibility, and so forth to measure the supplier’s low-carbon business operation.
Low-carbon innovation is used to evaluate whether the green supplier meets the requirements of the low-carbon supply chain. It should be able to guide and promote the alternative suppliers to pay more attention to environmental protection and carbon emissions reduction, make low-carbon strategy planning, and actively develop and use low-carbon technology to control business processes in energy consumption and pollution. The supplier should integrate the low-carbon concept into the daily operation and achieve good low-carbon performance. This main criterion includes 6 subcriteria, such as low-carbon image and green and low-carbon certifications.
This study integrates fuzzy set theory and grey relational analysis to solve the problem of green supplier selection in low-carbon supply chain. Grey relational analysis is used to calculate the weights of green supplier selection criteria, and fuzzy theory is used to evaluate and rank the alternative green suppliers according to the selected criteria. Also, a membership function of normal distribution is introduced to determine the membership degree of each selected criterion. The main steps and the detailed descriptions are depicted as follows.
The first step involves identification of the main criteria and subcriteria for green supplier selection. In Section
According to the main criteria and subcriteria shown in Table
The evaluation grade set is the set of the criteria’s evaluation results and indicated by
Here, we introduce a membership function of normal distribution to calculate the membership degree of each criterion. Usually, it needs to nondimensionalize the criteria in the traditional fuzzy method, which will bring about a lot of calculating work, especially when there are many criteria. However we do not need to nondimensionalize the criteria when using membership function of normal distribution. In addition, it is easier for us to find the problems in green supplier selection when we use membership function of normal distribution compared to traditional fuzzy method.
For a certain criterion, the evaluation value is different among different suppliers, while the evaluation value
Namely,
Suppose there are
Grey relational analysis proposed by Deng [
Determine the criteria set of importance. For
Determine the reference sequence. The reference sequence is determined through scoring the criteria’s importance by experts.
Calculate the grey relational coefficient. The grey relational coefficient
Here,
Calculate the grey relational grade. The grey relational grade directly reflects the pros and cons of the comparative sequence to the reference sequence and can be calculated by
Calculate the weights of criteria. Through formula (
For the weights of the 4 main criteria, we can get
Formula (
Compare the objects’ evaluation results and rank them, that is, to convert the comprehensive evaluation results
Company Z, founded in September, 2001, is a steel enterprise located in Changzhou, a city of Jiangsu Province in China. Now company Z has developed into a large-scale steel joint venture with annual steel production capacity of 11.8 million ton, which covers various industries of steel. Z has been certified by the ISO9001 Quality System Certification, the API-Q1 Quality System Certification of American association of Pipe, the ISO14000 Environment Management System Certification, and so forth.
For company Z, one of the important issues is how to reduce its carbon emissions to maintain market competitiveness and profit. In China, the government has set the target of carbon emissions reduction for the steel enterprises. If the steel enterprise’s total carbon emissions exceed the carbon emissions limit set by the government, it needs to buy carbon emissions rights from the carbon emissions trading center; otherwise it will face huge fines from government agencies. Under this condition, company Z needs to select its green supplier from a large number of suppliers in the low-carbon supply chain.
It is difficult for company Z to select its best green supplier from these potential suppliers. Firstly, although company Z has established the criteria for supplier selection, it did not establish the criteria for the green supplier selection in low-carbon supply chain. It is not appropriate to use the existing criteria to select green supplier. Secondly, company Z has to nondimensionalize the criteria in company Z’s previous supplier selection, which brings a lot of calculating work. Moreover, it is difficult for company Z to deal with qualitative criteria. Thirdly, the managers and practitioners’ understanding of the weights of criteria for green supplier selection are in the grey state, so it is suitable to use grey relational analysis to calculate the criteria’s weights. Fourthly, company Z usually used expert scoring method to select its suppliers, which is very subjective and is easily influenced by other factors. Therefore, company Z urgently needs an approach to select its green supplier in low-carbon supply chain management.
After referring to a preliminary list of criteria compiled using literature review on green supplier selection and the company Z’s actual situation, 10 managers, practitioners, and experts are asked to determine the green supplier selection criteria and allowed to discuss each criterion for clarification. Finally, it can be seen in Table
For company Z, it has a large number of suppliers. Taking the business development and needs to reduce carbon emissions into account, company Z only needs to select its green supplier from 12 main suppliers. The method proposed in Section
The evaluated values of 12 main suppliers.
Supplier | Enterprise low-carbon qualification |
Low-carbon production and service |
Low-carbon business operation |
Low-carbon innovation |
---|---|---|---|---|
Supplier A | 8.3 | 8.2 | 8.0 | 7.0 |
Supplier B | 7.8 | 8.7 | 7.3 | 7.2 |
Supplier C | 8.6 | 8.1 | 8.6 | 8.3 |
Supplier D | 8.4 | 8.6 | 7.4 | 7.7 |
Supplier E | 8.0 | 8.7 | 8.4 | 7.4 |
Supplier F | 9.0 | 8.3 | 7.8 | 8.2 |
Supplier G | 9.3 | 8.2 | 8.4 | 8.2 |
Supplier H | 7.6 | 8.0 | 8.4 | 8.3 |
Supplier I | 9.2 | 8.3 | 8.3 | 8.0 |
Supplier K | 8.4 | 8.7 | 8.0 | 8.1 |
Supplier J | 8.8 | 8.2 | 7.6 | 8.3 |
Supplier M | 8.7 | 7.8 | 8.5 | 7.9 |
From Table
The importance of the 4 main criteria determined by 10 managers, practitioners, and experts is listed in Table
The importance of the 4 main criteria.
Expert 1 | Expert 2 | Expert 3 | Expert 4 | Expert 5 | Expert 6 | Expert 7 | Expert 8 | Expert 9 | Expert 10 | |
---|---|---|---|---|---|---|---|---|---|---|
|
10 | 9 | 10 | 8 | 9 | 8 | 8 | 7 | 8 | 9 |
|
8 | 8 | 10 | 8 | 9 | 9 | 6 | 6 | 7 | 7 |
|
8 | 9 | 10 | 9 | 9 | 10 | 9 | 10 | 8 | 8 |
|
6 | 7 | 10 | 7 | 7 | 7 | 8 | 6 | 7 | 8 |
From Table
According to formulas (
In the end, we can conduct the fuzzy comprehensive evaluation by formula (
Based on the evaluation and selection above, supplier C is recommended as company Z’s best green supplier. In fact, company Z has given priority to supplier C in its new projects construction according to the results. Supplier E is recommended as the reserved green supplier.
Through the analysis above, it is easy to see the advantages and disadvantages of the alternative green suppliers and the field and direction that 12 suppliers need to improve. Based on the concept of continuous improvement, company Z can continue to improve its competitiveness and profitability in low-carbon supply chain.
With “United Nations Framework Convention on Climate Change” and “Kyoto Protocol” signed and entering into force, there has been broad consensus on carbon emissions reduction. So low-carbon supply chain management has become an inevitable choice for enterprises to cope with the pressure from the government and the market. It is one of the most important factors to select green supplier for the success of low-carbon supply chain.
In this paper, we propose a fuzzy-grey multicriteria decision making approach for green supplier selection in low-carbon supply chain. According to the demand of enterprises in low-carbon supply chain, 4 main criteria and 22 subcriteria are established for green supplier selection. A method integrating fuzzy set theory and grey relational analysis is also proposed. According to the principle that the criteria value will obey normal distribution in the case of large sample size, a membership function of normal distribution is also introduced to calculate the membership degree of each criterion. Due to that the people’s understanding of the weights of criteria for green supplier selection is in the grey state, so grey relational analysis is used to calculate the weight of each criterion. Fuzzy comprehensive evaluation is also used due to the nature of unquantifiable and incomplete information in green supplier selection. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade, and it is easier for us to find the problems in green supplier selection. A steel company Z is studied to verify the scientificity and feasibility of the proposed criteria and method for green supplier selection. The result shows that the proposed criteria and method have good applicability in practical situation. To some extent, the proposed method can be widely used as a structural model for green supplier selection.
It needs to be pointed out that the approach proposed in this paper is not suitable for green supplier selection when there are only a small number of potential suppliers or criteria. For further study, a fuzzy based questionnaire can be used for data collection in order to prevent information bias.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research was supported by the Fundamental Research Funds for the Central Universities (nos. 2015B24014 and 2017B43314).