Lean integration in maintenance logistics management: a new sustainable framework
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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