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In the emerging supply chain environment, green supply chain risk management plays a significant role than ever. Risk is an inherent uncertainty and has tendency to disrupt the typical green supply chain management (GSCM) operations and eventually reduce the success rate of industries. In order to mitigate the consequences, a fuzzy multi-criteria group decision making modeling (FMCGDM) which could evaluate the potential risks in the context of (GSCM) is needed from the industrial point of view. Therefore, this research proposes a combined fuzzy analytical hierarchy process (AHP) to calculate the weight of each risk criterion and sub-criterion and technique for order performance by similarity to ideal solution (TOPSIS) methodology to rank and assess the risks associated with implementation of (GSCM) practices under the fuzzy environment. The proposed fuzzy risk-oriented evaluation model is applied to a practical case of textile manufacturing industry. Finally, the proposed model helps the researchers and practitioners to understand the importance of conducting appropriate risk assessment when implementing green supply chain initiatives.

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In the previous decade, fossil energy resources shortage and environmental challenges such as air and water pollution, global warming, and greenhouse-gas emissions, etc. have increased environmental concerns considerably. Since, one of the most practical and useful solutions to decrease environmental pollutants is to deploy green purchasing and clean energies by organizations or even governments. Thus, the construction of renewable-energy power plants and, consequently, the green supplier selection for these plants’ equipment has become more important. With this respect, this article presents a novel approach to assess and select green suppliers of a solar power plant. The proposed approach integrates Fuzzy Analytic Hierarchy Process and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methodologies. The results demonstrate the efficiency of the proposed approach as a practical tool to assist managers and CEOs (Chief Executive Officers) of electric power industry in assessing suppliers of solar power plant’s equipment.

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Cost of Quality analysis is emerged as an effective tool for the industrial managers for pinpointing the deficiencies in the system as well as for identifying the improvement areas by highlighting the cost reduction opportunities. However , this analysis will be fully effective only if it is further extended to identify the cost incurred in ensuring quality in all areas of the supply chain including the hidden costs and costs of missed out opportunities. Most of the hidden elements of quality costs are difficult to track and not accounted by the traditional accounting tools. An exploratory analysis is made in this research to identify the hidden elements of quality costs in manufacturing industry. Further, the identified cost elements are classified into various groups for better analysis and, finally, prioritized to identify the vital few among them. Analytic Hierarchy Process (AHP) technique which is one of the most popular Multi Criteria Decision Method (MCDM) and Pareto analysis were used in this study for prioritizing the hidden quality cost elements based on their degree of impact on overall cost of quality. By this analysis, the key cost elements which are to be addressed to reduce the overall cost of quality are identified.