Bibliographic record
Abstract
The design and development of a product is a complex process, which requires many resources and various types of expertise. Because the process is complex, it is essential to estimate the design effort required to complete a product development project. The estimation of design effort is, in turn, a prime factor to predicting lead-time, cost, and effort requirements of a project. In this thesis, parametric models for estimating design effort are proposed. A case study involving engineering departments at Pratt & Whitney Canada (PWC) is presented. First, research is conducted on each of the design, aerodynamics, analytical and drafting departments at PWC to identify factors that could be best utilized in estimating design effort. Four factors are identified for parametric modeling. These factors are type of design, degree of change, concurrency, and experience of departmental personnel. The parametric model applied to each department uses least square regression. Furthermore, the jackknife technique is utilized to ameliorate the bias in regression equations coefficients. This research also uses a data masking technique in order to protect confidential data information of PWC. The masking technique enables to calibrate the impact of each factor considered in the parametric modeling, while not being affected by the masking. Data analysis is first utilized to establish regression based parametric models. Later, the regression equations are tested for their validation. It is found that the proposed parametric models provide good estimate of design effort when compared to the original estimates, with maximum relative errors of less than 10%. Furthermore, in each parametric model, the factors that significantly affect the design effort are identified using ANOVA table. Based on the outcomes reported in ANOVA, the number of factors is reduced and new models are developed with the reduced number of factors. Lastly, the application of the models, limitations and possible future studies are also discussed in this thesis.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".