How does big data influence smart manufacturing in the presence of preventive maintenance? A multi-analytical investigation
Purpose Smart manufacturing (SM) capitalizes on big data analytics (BDA) advancements by enhancing current capabilities such as defect identification and enabling supporting capabilities such as preventive maintenance (PM). The previous literature fails to investigate the comprehensive associations between SM, BDA and PM. Therefore, this study aims to investigate the relationship among SM, BDA and PM. Design/methodology/approach The present research implements a multi-analytical PLS-SEM-ANN approach to investigate the relationships among BDA, PM and SM. Findings This investigation indicates that BDA is an effective digital technology that positively affects the operations of SM and PM. Furthermore, the results suggest that PM has a positive influence on SM and that it also positively mediates the relationship between BDA and SM, where PM cannot be treated as an auxiliary practice and plays an important role in SM as a primary operation. Furthermore, implementing the BDA enhances the performance of SM and PM. Originality/value The role of PM in the context of BDA and SM has been ignored in past research, and this study offers novelty by examining this relationship.
Authors
- Ashutosh Samadhiya (ORCID: https://orcid.org/0000-0002-7613-7252)
- Jose Arturo Garza‐Reyes (ORCID: https://orcid.org/0000-0002-5493-877X)
- Sunil Luthra (ORCID: https://orcid.org/0000-0001-7571-1331)
- Farheen Naz (ORCID: https://orcid.org/0000-0002-2307-5975)
- Anil Kumar (ORCID: https://orcid.org/0000-0002-0557-5917)
Institutions
- London Metropolitan University (GB)
- All India Council for Technical Education (IN)
- University of Derby (GB)
- O. P. Jindal Global University (IN)
- University of Stavanger (NO)
Publication Details
- Journal
- Journal of Manufacturing Technology Management
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1108/jmtm-08-2024-0454
- Citations
- 3
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 1.05