Significant Factors of the Successful Lean Six-Sigma Implementation
Bibliographic record
Abstract
Based on an extensive literature review we have selected factors critical for Lean Six Sigma implementation success. Four variables were selected to be used as output variables measuring this project success: project on time completion, achievement of financial goals, sigma level achieved (that was measured using Defects per Million Opportunities, DPMO), and overall project success. Using empirical data from 256 Lean Six Sigma Projects, we present the model developed and identify significant factors for Lean Six Sigma implementation success. Empirical results, which were collected during Lean Six Sigma implementation in 39 business units of an Automotive Sector Company in North America and Europe, were analysed using Multivariate Analysis of Variance (MANOVA) and General Linear Model (GLM). Two main factors were found as positively linked with the different aspects of project success: the competency of the Black Belts team and the management support to the project.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".