The Use of Statistical Process Control in Total Quality Management: Bibliometric Analysis of Publication Performance and Research Trends
Why this work is in the frame
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Bibliographic record
Abstract
Total quality management (TQM) is a research topic that has attracted the attention of many researchers from the past until now because this concept is widely used for quality improvement strategies involving all organizational processes. This study presents a comprehensive bibliometric analysis to identify research patterns and trends in the scientific literature using Statistical Process Control (SPC) tools in Total Quality Management (TQM). Bibliometric analysis techniques are used for descriptive and performance analysis and research field mapping. Data was collected from the Scopus database with source-type journals of as many as 730 documents and analyzed using Bibliometrix on R software and VOSviewer. Statistical Process Control in Total Quality Management spans diverse fields like Medicine, Engineering, Business, Management and Accounting, Decision Sciences, and Nursing. Key publications appear in Pediatrics, Quality Progress, BMJ Open Quality, BMJ Quality and Safety, and TQM Magazine. Influential authors are Kaplan HC, Wang Z, and Wu Z. This field of research has been developed by researchers from countries such as the United States, United Kingdom, Canada, China and Taiwan. Cincinnati Children's Hospital Medical Center, Harvard Medical School, University of Washington, Johns Hopkins University, and McMaster University are the most relevant institutions. Recent studies emphasize quality improvement and statistical process control. This study benefits academics and practitioners investigating Statistical Process Control and offers a comprehensive overview of its role in Total Quality Management spanning the last 34 years. This research identifies the most influential authors, sources, affiliations, and countries in Statistical Process Control within Total Quality Management. Additionally, it illustrates the evolution of research in this field over time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.028 | 0.055 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it