A QFD-based decision making model for computer-aided design software selection
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
With the progress in technology and innovation in product development, the contribution of computer-aided design (CAD) software in the design and manufacture of parts/products is growing on significantly. Selection of an appropriate CAD software is not a trifling task as it involves analyzing the appositeness of the available software packages to the unique requirements of the organization. Existence of a large number of CAD software vendors, presence of discordance among different hardware and software systems, and dearth of technical knowledge and experience of the decision makers further complicate the selection procedure. Moreover, there are very few published research papers related to CAD software selection, and majority of them have either employed criteria weights computed utilizing subjective judgements of the end users or floundered to incorporate the voice of customers in the decision making process. Quality function deployment (QFD) is a well-known technique for determining the relative importance of customers' defined criteria for selection of any product or service. Therefore, this paper deals with design and development of a QFD-based decision making model in Visual BASIC 6.0 for selection of CAD software for manufacturing organizations. In order to demonstrate the applicability and potentiality of the developed model in the form of a software prototype, two illustrative examples are also provided.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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