Investing in lean manufacturing practices: an environmental and operational perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lean manufacturing practices (LMPs) and corporate environmental sustainability are becoming inextricably linked. Throughout the lean and green debate, many organisations have recognised that LMPs have implications for their sustainable development and competitive positioning. Not only LMPs are complex on their own, but when perceived from an environmental sustainability perspective, the decision to implement an LMP can become even more intricate. Although general tools exist, the lack of effective decision-making tools to help in the implementation of LMPs with an environmental sustainability dimension is palpable. Thus, this study tackles the aforementioned decision problem by incorporating environmental and operational performance outcome expectations as these expectations are viewed in light of the ease of implementation of various LMPs. A novel multi-criteria decision-making (MCDM) model for evaluation of LMPs is developed in this respect. The model integrates a three-parameter interval grey number with rough set theory and the TODIM method. The model is run using empirical data from six manufacturing organisations. The findings facilitate the identification of a ‘locus of investments’ for a better selection of LMPs. The robustness of the decision support model developed is assessed through sensitivity analysis.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 it