Lean integration in maintenance logistics management: a new sustainable framework
Bibliographic record
Abstract
The current industrial constraints on production systems, especially availability problems are complicating maintenance managers’ mission and making longer and further performance improvement process. Dealing with these problems in a wiser managerial vision respecting sustainability dimensions would be more efficient to optimize all resources. In this paper, and after addressing the lean/sustainability challenge in a the literature to define main research orientations and critical points in manufacturing and then maintenance specific context, two case studies have been conducted in two production systems in Morocco and Canada, within the objective to set a clearer scene of the lean philosophy implementation in maintenance and within the sustainability scope from an empirical perspective. To activate the social dimension being often non-integrated in the lean/sustainability initiatives, the article authors reveal an original research direction assigning maintenance logistics as the leading part of our approach to cover all sustainability dimensions. Furthermore, its management is discussed for the first time in a sustainable framework, where the authors propose a new model considering the lean/sustainable perspective and inspired by the rich Human-Machine interaction memory to solve daily maintenance problems exploiting the operators’ experience feedback.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".