Creating and Sustaining a Maintenance Strategy: A Practical Guide
Bibliographic record
Abstract
Manufacturing companies should create maintenance strategy and link it to the manufacturing and business goals but recent research in the North East of England suggested that few companies do this. It is unclear why this inertia existed but it could have been due to the complexity and variety of the advice on offer in relation to the formulation and implementation of strategy. The purpose of this paper was to provide a simple generic guide or roadmap for practitioners to follow. It began by highlighting the importance and benefits of a maintenance strategy and then considered literature appropriate to the topic. A key point arising from this review was that the three elements; process, content, context, need to be considered over the lifecycle of a strategy. Moreover, most strategic models converged to simple sequential models affording a generic functional process to be developed. This involved the integration of the “corporate hard systems” model and the “Plan, do, check, act, cycle“, forming a suitable maintenance strategy process. Accordingly, further guidance on policy assured the right “content”. The paper concluded with a short questionnaire used to audit the effect of “contextual factors” on maintenance strategy. The result was a comprehensive guide on how to formulate and implement maintenance strategy.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".