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Top1. Introduction
In today's competitive environment, the consideration on managing risk in a supply chain has become an emerging problem for the supply chain managers. The industries are facing a lot of risks because they are unable to predict exactly about the future circumstances and unanticipated events. The organization’s process for getting high efficiency level and dynamics of echo-supply chain are currently riskier than ever before (Samvedi et al., 2012). One of the vital factors of managing the risk process is the identification initially, afterwards estimation and quantification of risk. The main obstacle in estimating risks comes from the circumstances that there exists a high level of subjectivity in handling uncertain variables (Tang 2006; Wu & Blackhurst 2009). Most of the experts provide their inputs in the form of qualitative variables which mainly comes in the shape of subjectivity due to lack of accurate data. Yucenur et al. (2012) developed a modified VIKOR multi-attribute decision method for improving for insurance company selection but did not cover the holistic risk management in a supply chain.
In the emerging fuzzy business logistics environment, the absolute objective of structuring an efficient and effective supply chain makes it further susceptible to risk. In the modern world, the risk management in a supply chain is very complicated task, bringing about seriously contrary outcomes such as low-grade products quality, depreciation of plant and machinery, bad image of companies reputation, supply interruptions (Cousins et al., 2004), complexities among many stakeholders (Craighead et al., 2007) and severe falling off in the stock prices of companies (Hendricks & Singhal, 2005). Supply chain risk management (SCRM) has been considered as an emerging and widespread research area in industrial management disciplines. The review of literature (methodologies and algorithms) relevant to the measurement of supply chain risk in the operational term is not widely available (Heckmann et al., 2015; Lima et al., 2016). Some of the research works, regarding quantification of risk such as value at risk and conditional value at risk are classically fiscal in nature. The researchers incline to ignore essential supply chain processes achievements like customer service level and better quality of products (Lapide, 2000). Whereas taking in view the supply chain risk, some empirically designed research work has been completed (in the last few years) typically emphasizing on the operational issues and performances, like development of suppliers (Nepal & Yadav, 2015; Hashim et al., 2014; Viswanadham & Samvedi, 2013), inbound supply chain risk system (Ganguly & Guin, 2013; Garvey et al., 2015) and project management risk (Liang et al., 2018; Raghunath et al., 2018, Mhatre et al., 2017; Thuyet et al., 2007; Nazam et al., 2015) for numerous firms (Chen et al., 2013; Lücker & Seifert, 2017; Hashim et al., 2017).
This research work focuses on the content of (SCRM) by exploring the additional operational, social, upstream and downstream risks (outsourcing risk, process risk, environmental risk, global risk, technology risk, legal risk, market risk, financial risk, disruption risk and logistics risk, etc.) from a detailed review of literature and developing a combined fuzzy multi-attribute group decision making model to measure the supply chain risk. The suggested/developed risk quantification model has been illustrated by taking an interesting case from the supply chain of an aviation sector of Pakistan as a feasible case.
In this case study, the researchers developed and applied the extended FMEA based on VIKOR approach, which is used for multi-attribute optimization for complex systems, to obtain a compromise prioritized leveling of identified supply chain risks (SCRs) according to the selected risk parameters under fuzzy environment of aviation industry. The extended VIKOR approach is applied to measure risks in a holistic supply chain system and finally consolidate the values into a comprehensive risk index.