Structural model for sustainable consumption and production adoption-A grey-DEMATEL based approach

Sustainable Consumption and Production (SCP) patterns are becoming important in the implementation of sustainability in industrial contexts. In this sense, this study uniquely focuses on developing a structural model to evaluate the sustainable consumption and production adoption drivers and to improve sustainability aspects in the supply chain scenario under uncertain environments. Initially, fourteen drivers related to sustainable consumption and production adoption were selected from the literature and expert feedback. Then, the grey based Decision Making Trial and Evaluation Laboratory technique was used; this approach not only helps to identify the causal relationships between the selected drivers but also helps to evaluate the strength of their interrelationships. The findings indicate that ten drivers are considered influencing drivers and four drivers are called influenced drivers. Governmental policies and regulations to develop sustainable consumption and production focused system and Management support, dedication and involvement in sustainable consumption and production implementation have been found as the most influencing drivers and Gaining the market edge and improving the overall performance and Initiatives and promotional schemes regulated by various agencies in sustainable consumption and production implementation the most easily influenced drivers. This work features an Indian automotive case example to show the proposed model applicability. The finding of this work provide a structural support to the managers by knowing the cause (influencing) and effect group (influenced) drivers in sustainable consumption and production implementation in industrial supply chains. By knowing the cause and effect group drivers, managers can more easily analyze the relevant issues in sustainable consumption and production adoption on the shop floor and, consequently, will be better able to improve overall performance. Finally, the unique contributions and limitation of the work are highlighted to provide a foundation for future research. (C) 2017 Elsevier B.V. All rights reserved.