Understanding product lifecycle management and supporting systems
Bibliographic record
Abstract
Purpose This study attempts to expand knowledge of product lifecycle management (PLM) and supporting systems. Its objective is threefold: first, to identify and assess the impact of two key PLM building blocks on new product performance. Second, to use the aforementioned PLM building blocks to highlight the distinctive nature of PLM and closed‐loop PLM systems. Third, to demonstrate that the closed‐loop PLM system provides more new product benefits than the PLM system and that the usage of the closed‐loop PLM system is positively related to new product development. Design/methodology/approach The research hypotheses were tested on data collected from 87 manufacturers in the transportation equipment manufacturing industry in one Canadian province. Findings The findings show that only ten manufacturers have adopted a closed‐loop PLM system. As expected, the results show that the two key PLM building blocks, namely operational integration and information system (IS) usage, are positively related to new product development. Findings also show that the level of forward operational integration is similar in the closed‐loop PLM system and in the PLM system, while the level of backward operational integration, the usage of the PLM system and new product development are higher in the closed‐loop PLM system. Finally, the results demonstrate that the usage of the closed‐loop PLM system is positively related to new product development. Originality/value This contribution should give academics and practitioners alike a better understanding of the role and benefits of PLM and its supporting systems (the PLM system and the closed‐loop PLM system).
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".