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An integrated framework for evaluating the barriers to successful implementation of reverse logistics in the automotive industry

Reverse logistics (RL) strategy can have a positive impact on productivity, and the diminishing resources, along with the strict environmental regulations, have strengthened the need for this strategy. The purpose of this study is to develop an integrated framework for identifying: (1) the critical...

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Bibliographic Details
Published in:Journal of cleaner production 2020-11, Vol.272, p.122714, Article 122714
Main Authors: Kaviani, Mohamad Amin, Tavana, Madjid, Kumar, Anil, Michnik, Jerzy, Niknam, Raziyeh, Campos, Elaine Aparecida Regiani de
Format: Article
Language:English
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Summary:Reverse logistics (RL) strategy can have a positive impact on productivity, and the diminishing resources, along with the strict environmental regulations, have strengthened the need for this strategy. The purpose of this study is to develop an integrated framework for identifying: (1) the critical barriers to the successful implementation of RL in the automotive industry; (2) the importance and implementation priorities of these barriers; and (3) the causal relations among them. The proposed framework is composed of the Delphi method to identify the most relevant barriers, the best-worst method (BWM) to determine their importance, and the weighted influence non-linear gauge system (WINGS) to analyze their causal relationships. The proposed framework is applied to a case study in the automotive industry. The results indicate the economic barriers are the most important, and the knowledge barriers are the least important barriers to the successful implementation of RL in the automotive industry. •This study investigates the barriers to the implementation of reverse logistics.•The Delphi method identifies the most relevant barriers.•The best-worst method determines the importance of the barriers.•The weighted influence non-linear gauge system analyzes the causal relations.•A case study in the automotive industry demonstrates the applicability of the model.
ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2020.122714