The reverse supply chain has gained a lot of attention in recent years. Most of the companies do not consider the probability of occurrence of uncertain risks. This ignorance affects the company’s performance in various areas such as finances and unsatisfied customers. Companies are not aware of the losses faced by them due to lack of risk management strategies. This research is focused on identifying those risks that have a higher level of impact on the company and find ways to mitigate and control those risks in the reverse supply chain. This study provides methods of reducing key risks that would help businesses minimize losses both in terms of customer satisfaction and finances. Recently, many original equipment manufacturers (OEM) have been facing daunting challenges in terms of efficient and lean reverse logistics (RL) strategy due to the existence of inherent risks. These risks must be recognized and properly managed towards the successful establishment of efficient RL systems. In this research, a hierarchical reverse logistics risk structure representation has been developed so as to explore a formal model for qualitative risk assessment. The various parameters for defining risks have been presented. Further, the metrics for measuring likelihood and impact that aid to achieve consistent assessment have been studied extensively. An improved decision-making method using fuzzy set theory for converting linguistic data into numeric risk ratings has been attempted. In this study, the concept of ‘Left and Right dominance approach’(Chen and Liu, 2001) and Method of ‘In center of centroids’ (Thoran et al., 2012a,b) for generalized trapezoidal fuzzy numbers has been used to quantify the ‘degree of risk’ in terms of crisp ratings. Finally, a framework for categorizing different risk factors has been proposed on the basis of a distinguished range of risk ratings (crisp). Consequently, an action requirement plan has been suggested for providing guidelines for the managers to manage the risk successfully in the context of reverse logistics.
3.2 Introduction
Risk management plays a significant role in overall revenue of the company and thus net income in reverse supply chains. It has become significant and hence logical analysis of the impact and occurrence of risks involved in the reverse logistics supply chain activities needed. In today’s business sector, the tremendous change in business strategy results in increasing competition towards achieving a competitive advantage over lower costs and the ability to meet customer satisfaction through the use of reverse logistics. Companies are experiencing growing pressure from advocacy groups and non-
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governmental organizations and some customer related to their supply chains (Vachon and Klassen, 2006). Stakeholders demand corporate sustainability, non-financial accounting and reporting, procurement, supplier relations, (Nijhof 2002; Waddock and Bodwell 2004; Teuscher et al, 2006). Companies are increasingly expected to deliver a simultaneous balance of economic, environmental and social society and also result in long-term economic benefits. Organizations must look at improving and sustaining their Reverse logistics systems in order to remain competitive in a cost effective way as well as cope up with this unpredictable business situation. Therefore, the decision to carry out risk analysis in the reverse logistics functions has been proved beneficial towards gaining increasing advantage in the global market today (Abdullah & Verner, 2012).
A risk is a potential future loss or undesirable outcome that may arise from some present action. Risk factors are defined as a source that can pose a serious threat to the outcome. On the contrary, risk assessment is the determination of the quantitative/ qualitative value of risk related to a concrete situation and a well-recognized threat. Although some of the individual risk factors more significant than others, the reverse logistics success usually depends on the effective management all types risks, response strategies used to assess risks and an organization’s ability overcome them. Therefore, it is indeed necessary to develop a unified risk understanding model containing perceived risks in relation to RL system and factors that affect the manageability of these risks. Exhaustive literature review reveals that limited studies have been reported so far highlighting important sources of risks and associated risk influencing factors in the reverse supply chain (RSC). Moreover, it has been found out that limited attempts have been made to establish a comprehensive approach to analyzing various issues like risk assessment, mitigation, and devolvement of best practices in the perspective of RSC. Kou and Lu, 2013, have pointed out that individual knowledge; experience and intuitive judgement, provide a better assessment of risk than probabilistic approach. Hence, the authors have highlighted the applicability of fuzzy set theory for risk assessment in capturing the individual intuitive assessment. The aim this research is to develop a unified hierarchical risk model that can be used to estimate the degree risk extent efficiently and propose risk assessment procedure using fuzzy knowledge representation theory to support risk analysis. Furthermore, all perceived risks have been classified into different categories based on their quantifying value of risk ratings and also an action requirement plan recommended which could provide a guideline towards efficient management of RL risk. Our aim is not only finding the key risk, but also suggests the
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best possible solution to that particular risk. Hence, a step by step implementation of our solution, organizations will get benefits in terms of reverse supply chain activity improvement and thus reducing costs that may occur due to uncertainties in their overall supply chain activities.