The integration of lean, green and best practice business principles
Bibliographic record
Abstract
Background: Whilst there are separate streams of established research on lean, green and best practice initiatives, the intersection of these three strategic principles has not been addressed extensively in the past.Objectives: In this study a framework to integrate lean, green and best practice principles into an integrated business model was developed as a strategy for businesses to develop sustainable competitive advantages.Method: A descriptive case study was conducted on Toyota South Africa Motors (TSAM) to understand whether a clear link between the company’s environmental approach, lean principles and established best practice culture could be determined. In addition, the case study tested the view that the implementation of these three principles concurrently resulted in improved business results.Results: The main findings of the study revealed that TSAM’s commitment to lean, green and best practice business principles contributed and was directly linked to its business success in terms of sales and market position.Conclusion: It is recommended that businesses implement an integrated lean, green and best practice business model as a strategy to reduce costs and sustainably enhance profitably and competitiveness.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".