Purpose: The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.Design/methodology/approach: The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.Findings: The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.Practical implications: This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.Originality/value: The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

Purpose: In the era of Industry 4.0, conventional manufacturing supply chains are evolving into data-centric supply chains. Apart from it, organizations are facing a highly complex and dynamic market environment. Consequently, data analytics is emerging as a crucial tool for effective decision-making. Therefore, this study aims to explore the antecedents for the adoption of Big Data Analytics (BDA) in manufacturing firms and to examine its effect on organizational flexibility and supply chain resilience. Design/methodology/approach: Drawing from a comprehensive review of existing literature, a research framework has been created to explore the antecedents for BDA adoption and its impact on flexibility and resilience. Study is based on Technology-Organization-Environment (TOE) framework. Hypotheses are framed considering different constructs of the proposed research framework. Questionnaire-based survey was used to collect responses from organizations based in India. Partial least squares structural equation modeling (PLS-SEM) is used to test the proposed hypotheses. Findings: Findings show that technological, organizational and environmental factors significantly influence the adoption of BDA. Further, it is also observed that the successful adoption of BDA helps in improving the flexibility and resilience of organizations during disruptions. Research limitations/implications: Survey data was obtained from organizations based in India, which restricts the generalizability of results. Therefore, future studies may be carried out across different sectors and countries to compare and validate findings for robust managerial implications. Practical implications: Study has explored major antecedents for BDA adoption in organizations. Management may formulate a strategy based on insights for successful BDA adoption. Originality/value: Based on insights from study, organizations may improve their flexibility and resilience to sustain in a dynamic market environment and under various disruptions.