Factors Affecting Ship Design and Construction Lead Time and Cost
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
This paper reviews a ship design and construction case study in the context of the published literature on the design process and its impact on construction. The objective was to explore the factors that impact design and construction lead time and cost. Design and construction managers constantly experience pressure to accelerate the construction start time in an environment characteristic of frequent design changes and rework. Often the construction of the first ships of a series will aggressively overlap the design phase. This investigation assessed a case study that illustrated that as the degree of overlap between design and construction increases, design changes increased ship construction costs and duration. This negates the advantage of trying to reduce lead time by overlapping phases. Before strategies of overlapping are utilized, shipbuilders need to better understand the details of the design process and its integration with other functions to improve design quality and reduce the impact of design changes on manufacturing and construction. It is recommended that when overlapping strategies are considered, design changes and their impact on construction be factored into the decision. A better strategy would be to eliminate design quality issues and design and construction rework.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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