Bibliographic record
Abstract
Recently, global competition has led to shorter product life cycles and increased technological sophistication. Products are becoming more complex due to rapid technological developments and increasing consumer demands for lower costs, greater variety, and greater performance. At the same time the proliferation of new technologies is rendering products obsolete at an increasingly rapid pace. These market and technology trends lead to the emerging of concurrent engineering. This thesis firstly will give a definition and briefly introduction of concurrent engineering, including its fundamentals, and the benefits of concurrent engineering, its difficulties and caveats. After that this thesis will introduce an implementation method for concurrent engineering. This thesis will focus on concurrent engineering assessment model; the purposes of concurrent engineering assessment model are providing information about your current state of affairs. It describes how things are done now and how well they are being done. Firstly, two existing assessment models will be introduced. The existing assessment models are focused on the present situation; they only assess the present situation, they do not assess the past situation, and the future situation; most of these models look like a questionnaire, the assessment is highly subjective and not very accurate. This thesis will focus on constructing a mathematical assessment model, making the assessment much more objective and accurate. All in all, the major contribution of this thesis research is the constructing of the mathematical assessment model. This new model describes the history and the future of company, assessing the company's performance, exposing practical problems and identifying potential improvements.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .J53. Source: Masters Abstracts International, Volume: 45-01, page: 0436. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".