<p>This study aims to assess the critical factors affecting the adoption of Industry 4.0 technologies in the circular economy for future sustainability. The ISM Fuzzy MICMAC model has been used to validate the results on the variable for I4.0 implementation. This research collected data from academic and industry experts. A total of 15 variables were selected with the help of previous literature and expert opinion. The application of this study is in both industry and social development. The model divides construction into five levels, identifying key factors affecting Industry 4.0 adoption in circular economy performance. Key factors include production scalability, management support, operational performance, and investment cost. The MIMAC analysis identifies four groups, i.e., autonomous, dependence, linkage, and dependency variables. Investment cost, robust antistructure equipment, security, sustainable manufacturing technology, smart devices, quality, regulation policies, production recycling performance, safety, and trust are strong dependent variables. It provided new insight for practitioners and policymakers to make informed decisions, promoting critical consensus on inhibitors and promoting excellence in integrated production processes.</p>

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Factors affecting the adoption of industry 4.0 technologies in circular economy: interpretive structure modelling (ISM) approach for future sustainability

  • Rakesh Kumar,
  • Vinay Kandpal

摘要

This study aims to assess the critical factors affecting the adoption of Industry 4.0 technologies in the circular economy for future sustainability. The ISM Fuzzy MICMAC model has been used to validate the results on the variable for I4.0 implementation. This research collected data from academic and industry experts. A total of 15 variables were selected with the help of previous literature and expert opinion. The application of this study is in both industry and social development. The model divides construction into five levels, identifying key factors affecting Industry 4.0 adoption in circular economy performance. Key factors include production scalability, management support, operational performance, and investment cost. The MIMAC analysis identifies four groups, i.e., autonomous, dependence, linkage, and dependency variables. Investment cost, robust antistructure equipment, security, sustainable manufacturing technology, smart devices, quality, regulation policies, production recycling performance, safety, and trust are strong dependent variables. It provided new insight for practitioners and policymakers to make informed decisions, promoting critical consensus on inhibitors and promoting excellence in integrated production processes.