The Use of Statistical Process Control in Total Quality Management: Bibliometric Analysis of Publication Performance and Research Trends
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.151 | 0.209 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".