An imprecise goal programming approach for modeling design team's preferences in quality function deployment planning process
Bibliographic record
Abstract
Abstract The Quality Function Deployment (QFD) is an approach that facilitates designing product by analyzing and projecting the Customer's Needs (CNs) in the Engineering Characteristics (ECs) of a product. The aim of QFD planning process is to determine the target levels for ECs of a product that achieve high level of overall customers' satisfaction. However, integrating design team's preferences in this preliminary stage of product design could make the design more realistic and could also avoid unfeasibility in posterior phases of the product development processes. Moreover, this process is performed within an imprecise environment, and more than one factor must be taken into account in determining targets levels of ECs; especially, the limited resources and increased market competition. This paper presents an imprecise goal programming approach to determine the best aspiration levels of ECs in QFD planning process. Based on benchmarking data of ECs, the concept of satisfaction functions will be utilized to integrate explicitly the design team's preferences and incorporate the competitive analysis of target market into the modelling and solution processes. In addition, the relationships linking CNs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through an example of product development of an emulsification dynamite packing machine. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".