Six Sigma and Total Quality Management (TQM): similarities, differences and relationship
Bibliographic record
Abstract
Industries are continuously facing fierce competition and the challenge of meeting increasing demands for higher quality products at economic costs. The success of an organisation is directly related to how effective its implementation of continuous improvement (CI) is. For any manufacturing system, Total Quality Management (TQM) and Six Sigma are important CI methodologies. Effective understanding of these methodologies and their relationship will provide an industry with a competitive advantage. Many industrial organisations today are using either TQM or Six Sigma as the core for their CI efforts. There is a lot of dispute on which methodology is superior, how they relate to each other, what the common grounds are and what their differences are. As such, the relationship between TQM and Six Sigma is worth further investigation. In this paper, TQM and Six Sigma are introduced followed by a thorough comparison. More particularly, this work investigates their similarities, differences and how they relate to each other. Finally, this research introduces how they fit together in order to develop a new structure for integrating them together which will provide an improved approach for CI.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".