PurposeReverse logistics (RL), an inseparable aspect of supply chain management, returns used products to recovery processes with the aim of reducing waste generation. Enterprises, however, seem reluctant to apply RL due to various types of risks which are perceived as posing an economic threat to businesses. This paper draws on a synthesis of supply chain and risk management literature to identify and cluster RL risk factors and to recommend risk mitigation strategies for reducing the negative impact of risks on RL implementation.Design/methodology/approachThe authors identify and cluster risk factors in RL by using risk management theory. Experts in RL and supply chain risk management validated the risk factors via a questionnaire. An unsupervised data mining method, self-organising map, is utilised to cluster RL risk factors into homogeneous categories.FindingsA total of 41 risk factors in the context of RL were identified and clustered into three different groups: strategic, tactical and operational. Risk mitigation strategies are recommended to mitigate the RL risk factors by drawing on supply chain risk management approaches.Originality/valueThis paper studies risks in RL and recommends risk management strategies to control and mitigate risk factors to implement RL successfully.

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ERP began in the 1960s as material requirements planning, an outgrowth of early efforts in bill of material processing. However ERP Implementing as a new set of decision-making processes is a major undertaking involving member throughout the company, there are many barriers to implement ERP successfully. Organizations can reduce the effect of failure through identifying their strengths and weaknesses. One of the most significant methods for defect prevention is FMEA. Fuzzy logic as complementation of FMEA measures the degree of membership in a class instead of arguing over inclusion or exclusion. Fuzzy-FMEA is used as a preventive technique to decrease the failure rate in ERP implementation. The proposed Fuzzy-FMEA also identifies the major failure causes and effect of potential defects in ERP implementation by using fuzzy number. Then failure preferences can be characterized by the severity, occurrence and detection fuzzy values and overall fuzzy risk priority number.

Environmental issues and governmental regulations have pushed companies to be more environmentally conscious and socially responsible. Sustainable production plays an important role in the global markets and companies practicing sustainable manufacturing processes for protecting the environment can increase their competitiveness. Reverse logistics is one of the sustainable approaches, which returns back used product from point of consumption to the point of origin due to recovery and product reuse. The identification of critical success factors for reverse logistics is essential to facilitate reverse logistics organization in implementing it. In this work, critical success factors (CSFs) in reverse logistics were identified based on the critical success factors in supply chain management. A survey was conducted among reverse logistics experts to identify the factors.Then, the factors were ranked by TOPSIS, showed that transportation as the most important factor followed by process planning and resource efficiency. However, innovation was the lowest ranked.

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