Supplier Selection Study under the Respective of Low-Carbon Supply Chain: A Hybrid Evaluation Model Based on FA-DEA-AHP

Xiangshuo He, Jian Zhang
2018 Sustainability  
With the development of global environment and social economy, it is an indispensable choice for enterprises to achieve sustainable growth through developing low-carbon economy and constructing low-carbon supply chain. Supplier is the source of chain, thus selecting excellent low-carbon supplier is the foundation of establishing efficient low-carbon supply chain. This paper presents a novel hybrid model for supplier selection integrated factor analysis (FA), data envelopment analysis (DEA),
more » ... analytic hierarchy process (AHP), namely FA-DEA-AHP. First, an evaluation index system is built, incorporating product level, qualification, cooperation ability, and environmental competitiveness. FA is utilized to extract common factors from the 18 pre-selected indicators. Then, DEA is applied to establish the pairwise comparison matrix and AHP is employed to rank these low-carbon suppliers comprehensively and calculate the validity of the decision-making units. Finally, an experiment study with seven cement suppliers in a large industrial enterprise is carried out in this paper. The results reveal that the proposed technique can not only select effective suppliers, but also realize a comprehensive ranking. This research has enriched the methodology of low-carbon supplier evaluation and selection, as well as owns theoretical value in exploring the coordinated development of low-carbon supply chain to some extent. an approach to react to carbon emission reduction from the fountainhead, which can lower both the environmental governance and risk commitment burden. Thus, increased attention has been paid to the choice of low-carbon suppliers in the scientific researches. Throughout recent studies, supplier selection can be achieved by two steps: design an evaluation index system and propose an applicable model [9] [10] [11] . In the case of index system associated with supplier selection, the researches accomplished by Dickson and Weber have laid a momentous basis for the subsequent studies at home and abroad. Dickson [12] sorted out 23 indicators for supplier evaluation by a questionnaire and made a comparison on their importance, which pointed out that quality, delivery period, and historical performance were top three vital indexes. Weber et al. [13, 14] summarized 23 evaluation principles via 74 literature for 33 years and proved price, delivery period, quality, equipment and capability, geographical location, and technique were of great significance by means of statistical analysis. Hereafter, green supplier evaluation and selection has started with the choice of environment as an important factor. Hanfield et al. [15] adopted Delphi method to extract ten most momentous factors of supplier evaluation in green procurement including environmental records released to the public, Tier Two supplier environmental assessment, hazardous waste disposal, toxic waste pollution management, ISO14000 certification, reverse logistics projects, environmentally friendly product packaging, utilization of ozone-depleting substances, and administration of hazardous gas emissions. A novel evaluation index, namely hazardous materials, was proposed by Hsu and Hu [16] , which consists of green procurement, green material code and record, green design capability, harmful substance inventory and management, compliance as well as environmental administration system. When considering green image, product recycling, green design, green supply chain management, pollution control cost, and environmental performance, Yeh and Chuang [17] introduced multi-objective model to make a decision on green partner selection that focused on four goals, that is, cost, time, quality, and green assessment score. Carbon emission and carbon management involved in the study of green supplier option come into being along with the emergence of "Low-Carbon Revolution". Hsu et al. [18] pointed out that carbon information management system and related training were conducive to competitive supplier selection. The index system put forward by Shaw et al. [19] incorporated cost, quality rejection rate, delayed delivery rate, greenhouse gas emission, and demand. The research in supplier selection evaluation measures can be divided into two categories: One is qualitative analysis, such as intuitive judgement, negotiation, and bidding [20, 21] . Though these methods are relatively simple and easy to implement, most of them as based on empirical or certain deterministic criteria attached to strong subjectivity. The other is quantitative approach that can be further classified into three parts. (1) Multi-attribute decision-making method, such as analytic hierarchy process (AHP), analytic network process, multi attribute utility theory, the technique for order preference by similarity to the ideal solution. Felix et al. [22] aimed at taking both quantitative and qualitative influential factors into account and developed fuzzy based AHP to choose global supplier in the current commercial scenario. Kang et al. [23] utilized IC packaging company selection in Taiwan as an experiment to make a selection among suppliers by fuzzy analytic network. In order to effectively deal with decision maker's fuzzy goals in supplier option, a method integrated with multi attribute utility theory was presented in reference [24] . Sahin and Yigider [25] came up with an improved TOPSIS approach to handle complicated factors. (2) Programming method mainly encompassing linear programming, goal programming, and mixed integer programming. Nazari-Shirkouhi et al. [26] employed a multi-objective linear programming model to balance multi-price and multi-product in supplier evaluation. An approach for multi-choice and multi-segment goal programming and a mixed integer programming method were applied to cope with the supplier selection in reference [27] and [28] , respectively. (3) Artificial intelligence, containing neural network, genetic algorithm, support vector machine (SVM), and so on. Kuo et al. [29] designed a neural network based model to analyze quantitative and qualitative indicators in supplier decision. An integrated genetic algorithm was
doi:10.3390/su10020564 fatcat:adazvctying6tdsh34c2dsghte