A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain

Wang, Xiaojun, Chan, Hing, Yee, RWY and Diaz-Rainey, Ivan (2012) A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. International Journal of Production Economics, 135 (2). pp. 595-606.

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Abstract

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.

Item Type: Article
Faculty \ School: Faculty of Social Sciences > Norwich Business School
Related URLs:
Depositing User: Ivan Diaz-Rainey
Date Deposited: 04 Jan 2012 09:09
Last Modified: 21 Mar 2019 12:55
URI: https://ueaeprints.uea.ac.uk/id/eprint/35854
DOI: 10.1016/j.ijpe.2011.03.021

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