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This paper examines the dynamic interplay between Circular Economy (CE) and Supply Chain (SC). Further, this paper develops a framework indicating the transition from conventional SC to SC 4.0. As it addresses the separate bodies of literature on CE and SC 4.0, the study attempts to bridge the gap by examining barriers to SC 4.0 adoption in CE.

The article integrates bibliometric analysis with Total Interpretive Structural Modeling (TISM) and MICMAC analysis, thereby enriching the methodological rigour in investigating the barriers to SC 4.0 adoption within the CE context.

The paper provides insights into research trends, influential scholars, journals and prominent institutions through bibliometric analysis. Also, the findings identify four broad areas of driving, autonomous, linkage and dependent barriers to facilitate a comprehensive understanding of their impact and interdependencies.

The findings imply that effective policy interventions, enhanced management practices, and the adoption of technological innovations are essential for overcoming barriers to SC 4.0. The research recommends that stakeholders focus on fostering collaborative networks, building competencies in line with CE requirements, and leveraging big data for strategic supply chain decision-making.

This work contributes to the advancement of the circular digital supply field by consolidating research streams, uncovering innovation prospects, and shaping a well-informed research agenda. The distinct contribution lies in its categorization of these barriers into driving, autonomous, linkage, and dependent barriers, offering a novel perspective on the structural dynamics impeding the integration of SC 4.0 in CE.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.