Bibliographic record
Abstract
Organizations around the world have been implementing environmental management systems (EMSs) as an effective means to manage environmental performance. However, successful deployment of EMSs requires the effective integration of EMS considerations into existing core business functions. This has typically been challenging for many firms, as many business functions are typically not well aligned with EMS objectives. Process improvement, namely Lean concepts, is an example. The objectives of Lean concepts are very different from the objectives of EMSs, and certain differences in both systems create the potential for conflict. This report further explores the potential to integrate EMSs with Lean concepts. This report summarizes EMSs and Lean concepts across several comparable aspects including objectives, drivers, benefits, and implementation. The standards for each system, ISO 14001 and the Shingo Prize Model respectively, are also presented. After careful analysis and comparison of Lean concepts and EMSs, eight strategies are proposed to effectively integrate the approaches and succeed against individual system weaknesses. An integrated toolset of Lean concepts and methods with EMS considerations are also provided. The integration strategies are lastly discussed with respect to ISO 14001 and Shingo Prize Model requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".