TY - JOUR
T1 - Interpretive model of enablers of Data-Driven Sustainable Quality Management practice in manufacturing industries
T2 - ISM approach
AU - Singh, Mahipal
AU - Rathi, Rajeev
AU - Antony, Jiju
N1 - Funding Information:
According to the ISM model (refer to ), ‘Top management commitment and enthusiasm (E6)’ and ‘Financial support (E11)’ are found at the bottom level (VIII), which means the most prominent and driving enabler. Both enablers ‘E6’ and ‘E11’ mutually influence each other, which reflects that the implementation of a DDSQM system requires the involvement of everyone within an organization as well as healthy financial resources. Bouranta et al. () also found that the successful implementation of an effective QMS largely depends on the engagement of top management (Bouranta et al., ). Moreover, in emerging economies like India, funding is a crucial component, as DDSQM requires a large investment. Companies in emerging economies can raise funds to develop DDSQM infrastructure by introducing reward-based financial support. In the above level (VII), the enabler ‘Application of advanced quality analytics (E14)’ and ‘Adoption of data-driven lean and green initiatives (E5)’, are found to mutually stimulus to each other and based on enabler ‘E6’ and ‘E11’. With the ability to track both quality and performance, E14 allows companies to identify, document, and monitor the most relevant customer interactions and detect the reasons behind performance failure. This finding is supported by Sanchez-Marquez et al. (), who found that applying data analytics helps to establish sustainable QMS in the era of Industry 4.0 (Sanchez-Marquez et al., ).
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Data-Driven Sustainable Quality Management
KW - developing economy
KW - enabler
KW - interpretive structural modeling
KW - manufacturing industry
KW - MICMAC
UR - http://www.scopus.com/inward/record.url?scp=85142122097&partnerID=8YFLogxK
U2 - 10.1080/14783363.2022.2132141
DO - 10.1080/14783363.2022.2132141
M3 - Article
AN - SCOPUS:85142122097
SN - 1478-3363
JO - Total Quality Management and Business Excellence
JF - Total Quality Management and Business Excellence
ER -
