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The ANP-MOP green partner selection and supply chain construction model proposed in this research has the following characteristics:

1) It is designed specifically for green partner selection and supply chain construction, enabling an in-depth analysis to be conducted within the green decision-making environment. Supply chain managers can apply it directly without further adjustment to fit the GSC decision-making environment. As such it is an advance on other existing partner selection models/methodologies which are designed only for more general decision-making situations (for example, Humphrey et al., 2007; and Ng, 2008). Whilst the proposed model designs for green partner selection and supply chain construction, but it does not focus only on green related criteria. Rather it considers both green and business criteria at the same time. This is an advance on other existing models/methodologies for green

partner selection and supply chain construction, such as the models/methodologies proposed by Noci (1996) and Awasthi et al. (2010). This characteristic overcomes the limitations of models which only pay attention to green related objectives whilst neglecting traditional business attributes. For instance, in the fuzzy multi- criteria model presented by Awasthi et al. (2010) for evaluating suppliers, there are only twelve environmental criteria in total. However, in the ANP-MOP green partner selection and supply chain construction model proposed in this research, four clusters which contain both green and business criteria were constructed. This not only represents an advances in research on green partner selection and supply chain construction but also a means of improving efficiency and effectiveness in GSC business practice.

2) The proposed model’s use of ANP methodology enables a good balance between both green and business criteria to be achieved when selecting potential green partners. Based on the inputs of professional experts, the proposed ANP sub- model enables differential consideration to be given to the different clusters of criteria (Saaty, 1996; Kuo et al., 2010). This is an advance on existing models such as the optimum mathematical planning model for partner selection in GSCs proposed by Yeh and Chuang (2011), in which four main objectives are treated equally. In contrast, the proposed ANP sub-model can balance the different green criteria and business criteria more reasonably and efficiently.

3) The proposed model introduces new and potentially very important performance indicators in the form of the environmental difference, the business difference and the eco-efficiency ratio. These can help decision-makers in GSCs to quantify the trade-offs between environmental and business performance. They also have the potential for use by national and local policy makers and legislators to help formulate and adjust environmental regulations for businesses as well as providing guidance to specific industrial development (Walton et al., 1998). For instance, in the electrical industry, as highlighted by the results of the empirical illustrative application in this paper, the eco-efficiency ratio is relatively low. Therefore, the internal incentive within the industry to achieve a more green supply chain is limited. This would offer explain, at least in part, why environmental regulations

and restrictions for this industry have been made stricter (e.g. with the introduction of WEEE and RoHS).

4) Compared to existing research such as Lee et al. (2009)’s fuzzy AHP partner selection model and Chen et al. (2012)’s GSC strategy-selection model, the proposed ANP-MOP green partner selection and supply chain construction model can achieve the goal of partner selection and supply chain construction simultaneously. Thereby, the efficiency of decision-making of partner selection in GSCs can be improved significantly. For example, by applying Lee et al. (2009)’s fuzzy AHP partner selection model, managers in GSCs would only be able to identify the best potential partners. To complete the supply chain construction and lot-sizing problem, the managers would also have to apply other managerial tools and/or models. However, the ANP-MOP green partner selection and supply chain construction model proposed in this research can accomplish the above two tasks simultaneously. Thus, the proposed model can enable decision-makers in real practice to identify the most suitable potential partners and construct the optimal GSC structure at the same time.

5) The proposed model is effective and efficient for green partner selection and supply chain construction. On the one hand, as the illustrative application and the sensitivity analysis show, the proposed model is designed to solve the green partner selection and supply chain construction simultaneously, thereby improving the efficiency of partner selection and supply chain construction (Wu and Barnes 2009, 2012). On the other hand, the results of the two decisions can be mutually corroborated if they are made at the same phase. If we separated these decisions into different phase, if inappropriate or even wrong decisions on the previous phase were made, it would transfer to the following decision-making phase, which is bound to reduce the effectiveness of the whole decision-making process. Furthermore, finding problems in the second decision-making phase would prompt the need to return to the previous phase, thereby affecting the efficiency of decision-making overall.

6) The numbers of objectives/criteria and constraints in the proposed model are flexible. Firms wishing to adopt the proposed model could select their own

objectives/criteria and constraints to suit their own decision-making circumstances and demands. The choices of how many and which objectives/criteria and constraints to use rely both on firms’ preferences and judgement on their competitiveness and the decision-making data/information availability. Thus, they can decide to remove or add objectives/criteria and constraints as they deem appropriate. According to the conceptual model proposed by Wu and Barnes (2012), building a set of customized criteria is a prerequisite step in partner selection and lot-sizing decision-making. Managers in GSCs could apply systematic methods (e.g. Lin and Chen, 2004; Wu and Barnes, 2010) to construct their own optimal customized criteria before final selection decision-making.

6. Conclusions

In presenting a model for green partner selection and supply chain construction, this paper makes the following contributions: Firstly, the proposed model can enable organisational decision-makers to simultaneously meet the highly desirable objectives of both minimizing the negative environmental impact of the supply chain whilst maximizing business performance. This is an advance on existing methods, which do not offer this capability. Secondly, the proposed model can achieve the goal of partner selection and supply chain construction (i.e. the lot-sizing problem) simultaneously. Thereby, managers of GSCs can identify the most suitable potential partners and construct the optimal GSC structure at the same time. The results of these two decisions can be mutually corroborated if they are made during the same phase of decision-making. Thirdly, the proposed model combines two established techniques, namely analytic network process (ANP) and multi-objective programming (MOP) methodologies, to a new context, namely that of meeting environmental goals in business. Whilst the use of a model based on a combination of ANP and MOP for supply chain partner selection is not new (Wu et al., 2009), this is the first time that such a model has been applied to the green partner selection and supply chain construction problem. As such, this represents an advance on existing approaches, as it offers a new way of solving the green partner selection and supply chain construction problem effectively and efficiently. Fourthly, through the extension of the proposed model to develop the concepts of the environmental difference, the

business difference and the eco-efficiency ratio, the paper provides an additional tool for decision-makers to quantify the trade-offs between environmental and business performance. Furthermore, the eco-efficiency ratio could also be used by government and other regulatory bodies to help them to adjust the intensity of their environmental regulations in order to improve the environmental performance of specific industries. Last but not least, the paper makes an additional contribution through the use of the ANP sub-model, which can balance the different green criteria and business criteria more reasonably and efficiently. This is an advance on existing models, such as the optimum mathematical planning model for partner selection in GSCs, in which main objectives are treated equally.

It is possible to identify some shortcomings in the proposed model. Firstly, it does not consider the uncertainty both of costs and customer demand. This could lead to lower sensitivity in decision-making in its application in high uncertainty environments. Secondly, the complexity and number of PWCs required increases markedly as the numbers of factors and clusters increases. Therefore, formulating and selecting the most suitable number of factors and clusters becomes a problem that needs to be tackled in the first instance (Wu and Barnes, 2010). Thirdly, using the weights to combine the different objectives is one of the features of this multi-objective decision- making problem. Comparing with other non-weights combination methods, such as AHP, ANP, and ANN, the MOP sub-model is a single objective problem in nature. This is also one of the reasons why this research includes ANP at the first stage to minimize this limitation. Fourthly, because the measurement and quantification of environmental influence along the supply chain it is out of the scope of this paper, only illustrative data rather than real data were used in the MOP sub-model illustration. Furthermore, due to space limitations, this research does not provide a more complex illustration application example. Doing so, could be one of the directions for future research. Finally, applying ANP methodology requires a significant input of resources and the participation of relevant experts, especially when there is a large number of PWCs to undertake. This makes the proposed model more suitable for strategic decisions. However, as MOP methodology has enough flexibility, more frequent lot-sizing decision-making can be easily undertaken by the proposed model.

Most research in this field, including that presented in this paper, evaluates suppliers only from the buyer’s perspective (Li et al., 2009; Ho et al., 2010). Further research is required to provide the flexibility to incorporate the perspective of the supplier in the application of the model. This would provide additional insights to help identify the attributes that suppliers need to focus on in order to become preferred suppliers (Bai and Sarkis, 2010b). Additionally, more research could be undertaken in order to incorporate a consideration of the environment in which decision-making takes place. Finally, the research on the eco-efficiency ratio as presented in this paper is just in its infancy. Further development and application of the eco-efficiency ratio is a key direction for future research.

Acknowledgements

This work was financially supported by ‘the National Natural Science Foundation of China’ (No. 71202058), ‘the Natural Science Foundation of Fujian Province of China’ (No. 2012J01305), and ‘the Specialized Research Fund for the Doctoral Programme of Higher Education’ (No. 20110121120028). The authors wish to thank Professor Stefan Seuring and three anonymous reviewers for their valuable suggestions and criticism to improve the paper.

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