Using the Management Control System to Develop a Sustainable Six Sigma Program
Bibliographic record
Abstract
This paper presents an implementation strategy for companies considering a Six Sigma continuous improvement program. The proposed model was developed combining results of a study on the best practices of twelve organizations in Canada that have been operating a Six Sigma program for more than two years and the ideal Management Control System (MCS) for Six Sigma companies as presented in the literature and proposed by a quality management expert’s focus group.The financial returns from the upfront investment of implementing a Six Sigma program can only be realized over a number of years. The maximization of those returns will be reached only when Six Sigma improvement tools and techniques receive rapid, organized deployment and are in everyday use by all employees. That is, when Six Sigma is no longer viewed as a standalone quality initiative but is an integral part of the corporate culture.The results of the case study on the Canadian organizations conclude that corporations are making their Six Sigma program part of the 'corporate fabric' by integrating that quality program into their Management Control System. The paper will be of interest to academics, accountants, quality practitioners and senior manager.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".