Antecedents and outcomes of manufacturability in integrated product development
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a model linking the role of design engineers to shared team knowledge, enhanced manufacturability, and product development outcomes. New product manufacturability is a quality of the product design that indicates the ease and reliability by which an organization develops products by using its manufacturing and supply chain resources. Design/methodology/approach The model is tested using a sample of 205 product development projects from firms in the USA and Canada. Findings The findings of the large‐scale empirical study suggest that by facilitating informing practices among functional specialists, design engineers help translate a functional portrayal of the product in terms of customer attributes, to a form description in terms of engineering characteristics, and then to a fabrication view in terms of manufacturing processes. Practical implications New product manufacturability can be a distinctive competency that provides competitive advantage by lowering costs, improving customer value, and speeding products to market. Originality/value In contrast to previous research that showed that role changes of design engineers have a narrow impact on development productivity (e.g. improving resource allocation), this paper suggests that these role changes of design engineers have a much broader impact on manufacturability and, through this, improve manufacturing cost, time‐to‐market, and value‐to‐customers.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".