مدل AHP فازی دو مرحله ای برای ارزیابی ریسک اجرای طرح های سبز در زنجیره تامین مد

Green or environmental concerns are drawing more and more attention both in academia and industry. Careful deployment of green initiatives or policies could not only fulfil the requirements of environmental legislation but also lead to a competitive advantage for firms. Nevertheless, making optimal decisions in this regard is not easy. This is principally due to two reasons: (1) the qualitative nature of, and (2) the uncertainty associated with, the parameters involved in the decision-making process. Analytic hierarchy process could be a useful tool to tackle the first challenge because of its ability to handle both qualitative and quantitative variables (decision criteria). Unfortunately, this approach is inadequate at addressing the uncertainties common in real-life applications. This challenge is particularly noticeable in the fashion industry since demand is very volatile, and there are many uncertain variables associated with the whole supply chain. As a result, this paper blends fuzzy logic, which is a popular method of incorporating uncertain parameters into the decision-making process, with analytic hierarchy process to form a selection (decision-making) model for different green initiatives in the fashion industry. The rationale behind the model is to analyse the associated risk of different alternatives, subject to different factors, be they deterministic or not. A numerical example is included in this paper to demonstrate how the proposed model works.

Environmental concerns from consumers, governments and academics have encouraged businesses in the fashion sector to introduce and promote business practices that help to ease the negative impacts of their actions on environment. These environmentally conscious practices have been widely reported in the literature ranging from eco-design, green procurement, environmentally friendly packaging and transportation, to the various product end-of-life practices such as recycling and remanufacturing (Sarkis, 1995, Sarkis, 1998, Carter and Carter, 1998, Rao and Holt, 2005 and Yung et al., 2009). The implementation of these green initiatives could generate higher revenues in the fashion industry as retailers' green credentials are becoming an important differentiator that enables firms to secure greater customer loyalty. It also increases cost savings by cutting energy consumption and packaging waste in times of rising input costs, with rising commodities and energy costs being a particular concern. Deploying a proper green initiative or policy could generate a competitive edge for a company (Sarmiento and Thomas, 2010). Recent technological development like Radio Frequency Identification Technology (RFID) could provide companies with many opportunities to take green issues into consideration, although cost may always be a more important evaluation criterion (Gaukler et al., 2007). Nevertheless, it is equally likely that deploying a poor green initiative might lead to considerable problems if implementation is not managed well (Cheng et al., 2008). Implementation of greener designs/practices is likely to face potential adjustments in their internal and external operations. With the trend of increased collaboration with international supply partners and expended supply networks, adopting greener practices could also increase the probability of experiencing adverse events in supply chains that significantly threaten normal business operations of the organisations in the supply chain. These problems could include an increase in total costs and reputation risk from failures along the supply chain. It is, therefore, clear that just like any strategic policy change, implementing green initiatives consists of a certain degree of risk, and hence a proper risk assessment tool is needed. While more pressures are emerging from stakeholders to prompt companies and the entire fashion supply chain to adopt green supply chain practices, little effort has been paid on assessing the risk involved in implementing various green initiatives for managerial assessment purposes. A greener product design may improve brand image and stimulate demand from ‘green consumers’ (Peattie, 2001). However, this transition might require the use of new technologies in supply and production processes, as well as the development of new quality systems. Purchasing-wise, it might need the procurement of new raw materials and affect the supplier selection process. Logistics-wise, it might require new inbound and outbound logistics along with new packaging. Meanwhile, there is no guarantee of saleability and future growth in the wide market. Accordingly, it is important to use an integrated heuristic approach and analyse the risk involved in a supply chain context, thus enabling decision makers to understand the capabilities and resources that need to be deployed so as to successfully implement a ‘green’ supply chain in the fashion industry. Making such judgement, however, is never an easy task as there are many qualitative factors concerned with the decision-making process. In the literature, analytic hierarchy process (AHP) is a widely employed methodology to facilitate this kind of process. Notwithstanding this, traditional AHP is unable to deal with another realistic concern: uncertainty. Without uncertainty, one may argue risk assessment is not necessary. Uncertainty is a particular issue in the fashion industry since demand is highly volatile (Wang and Chan, 2010). In view of this, a decision model that couples AHP with fuzzy logic, which is used to incorporate uncertain variables into the proposed model, is developed in this paper. The rest of the paper is organised as follows: Section 2 presents a review of relevant literature. Section 3 formulates the blended fuzzy AHP approach, including its mathematical derivation. Then, a numerical example is presented in Section 4 to demonstrate how the model works. Section 5 concludes this paper.

Facing increase in pressure to take green issues into consideration, there is a need for firms to react accordingly. It is important to be aware of the tools that can help them to make associated decisions. The purpose of this study was to develop a risk assessment model enabling a structured analysis of aggregative risk of implementing various green initiatives in the fashion industry supply chain. A hierarchical structure model was developed addressing diverse aspects associated with the implementation of green initiatives in fashion supply chains. The quantitative scales were expressed by TFNs to capture the vagueness in the linguistic subjectivity of risk factors. Fuzzy AHP is used to calculate the aggregative risk index (ARI). Numerical examples were presented where the application of the model was illustrated with three case scenarios. The ARI gives an indication of overall risk level for implementing a particular green initiative. The relative weights and the quantified risk for sub-criteria through the fuzzy approach help to identify key critical factors that contribute to the ARI. The results could be used to support the decision-making process when fashion companies consider adopting green practices in order to reduce the negative environmental impact while maintaining operational and economic efficiency. It provides a practical solution by which fashion companies can systematically assess the risks involved when moving to a greener fashion supply chain and identifying the potential gaps in resources and capabilities. Therefore, necessary actions can be taken into account to address them. This paper provides an option for decision-makers to analyse the risk of deploying different green initiatives. Although the model is built based on the fashion industry, it can be extended to other industries by fine-tuning the model (e.g. including or replacing some criteria or sub-criteria). As long as the hierarchical structure is preserved, the model is still applicable virtually to any industry. Compared to the existing fuzzy AHP based multiple attribute decision-making systems the two-stage fuzzy AHP model outlined in this paper is simple, being less demanding with respect to the computational power and time needed to make a decision. In fact, most numerical calculations were computed using Excel spreadsheet (see Fig. 3). While the use of the model does not involve cumbersome mathematical operation, the fuzzy-AHP model is tractable enough to capture the vagueness of uncertainty and vulnerability within the supply chain, as well as to provide the efficiency and flexibility to tap the subjectivity and preferences of decision makers.Despite the tangible benefits to the model outlined in the paper, there are some limitations and weaknesses of the model. Some are general problems associated with risk assessment modelling, while others are specific to the model. The main challenge of this research is to provide a single value risk index (ARI) to represent the overall risk level of implementing green initiatives in the fashion supply chain. All criteria and its associated sub-criteria in main assessment areas have to be accounted and accumulated in the assessment. The complexity of the model lies in establishing the degrees of risk g(s, p) of all sub-criteria, in which a long list of values (grades of risk factors) must be provided before the risk quantification is made. In addition, users have to make subjective decisions when conducting pair-wise comparisons to obtain weights for risk attribution and deciding the scales of risk factors. In fact, the functionality of the model is highly dependent on the knowledge, expertise and communication skills of assessors. The assessment results are most comparable when the same assessment team performs all the assessments. Furthermore, the hierarchical structure model was developed based on Chan and Chan's (2010) AHP model for supplier selection in the fashion supply chain and Sarmiento and Thomas' (2010) AHP approach to identifying areas for improvement when implementing green initiatives. A generic model was constructed to address diverse aspects of the fashion supply chain and analyse the risk associated with implementing green initiatives in the chain. However, within the fashion industry, different challenges and risks may be imposed on individual organisations depending on their market segmentation and operational objectives. A hierarchical structure model that is specifically developed or modified to reflect the nature of the business will improve the decision-making process for fashion firms that are considering the implementation of green initiatives. Further, the model can be extended to enable life cycle assessments covering all aspects associated with the implementation of green initiatives in fashion supply chains (e.g. reverse logistics). This is certainly an area the authors would like to research in the future. Another limitation of the proposed model is in relation to the fuzzy membership function. Obviously, TFNs are not universal and some other forms of membership functions may be required in other applications. Fortunately, the same philosophy in terms of fuzzification and defuzzification can be employed, so that even if other fuzzy membership functions are presented, the model can also be modified to accommodate that. Of course, further work is needed in doing so. Finally, future research should focus on applying the model based on real-life applications. The model outlined in this paper has been developed from theoretical research perspective; accordingly it may overlook important issues that only empirical applications can highlight.