Interpretive model of enablers of Data-Driven Sustainable Quality Management practice in manufacturing industries: ISM approach

The fourth industrial revolution and updated government regulations on NET zero have enforced manufacturing organizations to adopt sustainable practices in their system. Also, manufacturing units need to deal with huge data sets to sustain the quality of products. In this regard, Data-Driven Sustainability Quality Management (DDSQM) is an interdisciplinary approach that provides an understanding of big data management with due quality and sustainability in manufacturing settings. Regardless of its potential benefits, manufacturing firms in developing economies remain reluctant to follow DDSQM practices. To persuade organizations for adopting DDSQM practices in real-time needs to explore the enablers with their contextual relationship for its successful initiation. In the present study, DDSQM enablers are identified and screened through literature and expert opinions from manufacturing industries. Thereafter, screened enablers are modeled through Interpretive Structural Modeling (ISM) and clustered via MICMAC analysis. The proposed methodology was executed with experts from academics and industries in developing economies. This study constitutes the first strive to explore the contextual relationship among enablers of DDSQM practices in developing countries’ manufacturing industries. The findings can help policymakers of emerging economies to adopt data analytics, quality management, and sustainable practices, that in turn, facilitate the implementation of DDSQM practices.