The Power of ROFO Principle Together with Companywide Training in Executing Lean Production Strategy
Bibliographic record
Abstract
This paper reports the findings of the case study conducted at Schaffner Thailand (ST) factory regarding the application of the ROFO principle coupled with companywide training on the execution of Lean Production (LP) strategy. The case study was motivated by 3 main objectives: 1) to examine the effectiveness of the ROFO principle and companywide training on the execution of LP strategy, 2)to study whether there were significant improvements in productivities between periods I and II, and 3) to assess whether ROFO principle had influenced significantly in changing the mindset of the staff. Companywide training was carried out on 3 modules: the ROFO principle, 5S and Lean Production (LP) concepts. The training of the 3 modules was implemented in period II (2008 to 2012) but not in period I (2003 to 2007). The methods used were survey, interview, and observations. The findings fully support the 3 objectives. The results were encouraging as productivities were not only improved in period II but also the willingness mindset of the staff. This is the power of the ROFO principle as each cycle of the ROFO principle resulted in a chain of corrective actions and learning.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".