Process considerations for Product Lifecycle Management implementation for high-value Engineering-to-Order programmes
Bibliographic record
Abstract
Abstract This paper presents the process considerations contained within the first ever framework for implementing Product Lifecycle Management (PLM) within high-value Engineering-to-Order (ETO) programmes. The scientific contribution of the research is the identification of the process-oriented factors that are instrumental in the successful implementation of PLM within an ETO context. The framework has been developed using a qualitative methodology based on the thematic analysis of 27 semi-structured interviews. The participants were senior personnel from 11 ETO organisations in the United Kingdom, France, Australia, the United States and Canada. The thematic analysis resulted in framework themes described in relation to the process objectives, challenges or enablers, and the contributing elements of the themes were then synthesised to illustrate their interconnectedness in supporting PLM implementation. Validation of the framework using 19 participants selected from seven ETO organisations resulted in 95% agreement with statements that assessed the quality, structure and versatility of the framework. This research contributed to the updated BAE Systems Maritime Naval Ships PLM strategy for the design, build and in-service support for the First of Class new generation Royal Navy vessel for a recent shipbuilding programme.
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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 | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".