Bibliometric Analysis for Quality Management in The Manufacturing Sector: 2020 to 2024
In today's dynamic manufacturing market, quality management is critical to competitiveness, efficiency, and customer happiness. This study includes a bibliometric analysis from 2020 to 2024 to illuminate trends, patterns, and developing quality management areas in the industrial sector. Using bibliometric methodologies, this study examines a wide range of scholarly outputs, including journal articles, conference proceedings, and other relevant material, to determine the intellectual structure of this field. This methodology finds crucial contributors, seminal works, research focal points, and emerging themes in the field by quantitatively examining publication outputs, citation networks, collaboration patterns, and keyword co-occurrences. By synthesizing and visualizing the bibliographic data, this study provides a comprehensive picture of the knowledge landscape, allowing researchers, practitioners, and policymakers to navigate the complexities of quality management in manufacturing and foster continuous improvement initiatives. Furthermore, this bibliometric research provides important insights into the evolution of quality management techniques, emphasising the adaptation of methodologies and frameworks in response to growing difficulties and technological advancements. This study provides a road map for future investigations to improve quality management techniques in the industrial industry, indicating significant research gaps and areas ripe for discovery. Furthermore, the analysis gives information on the global distribution of research efforts, allowing for international collaborations and knowledge exchange to propel collective growth in quality management. Finally, this thorough review of the scholarly literature broadens our understanding of manufacturing quality management. It informs strategic decision-making processes for businesses looking to optimise their operations and provide superior products and services to customers.
Authors
- Astri Ayu Purwati (ORCID: https://orcid.org/0000-0002-6705-6020)
- Siti Norliyana Harun (ORCID: https://orcid.org/0000-0002-7253-3637)
- Amaliyah Amaliyah (ORCID: https://orcid.org/0000-0002-5457-112X)
- Mazzlida Mat Deli (ORCID: https://orcid.org/0009-0003-9057-8518)
Institutions
- Universitas Airlangga (ID)
- Institut Bisnis dan Teknologi Pelita Indonesia (ID)
- National University of Malaysia (MY)
Publication Details
- Journal
- Journal of Applied Engineering and Technological Science (JAETS)
- Published
- 2025-06-08
- DOI
- https://doi.org/10.37385/jaets.v6i2.6454
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universiti Kebangsaan Malaysia