Bibliographic record
Abstract
New Product Development - NDP is a major source of competitive advantage to companies. For decades researchers have studied the phenomena and various approaches have emerged over the years. Firstly, NPD was structured in clearly defined phases or stages to enable a quick and risk-free flow from idea to launch. Later, Concurrent engineering - CE, in which critical development phases are performed simultaneously, was successfully introduced by Japanese companies like Toyota. In recent years, CE has become a widely used option world-wide. CE proven benefits include reduced time-to-market; reduced human and capital cost, increased product quality; all factors related to project efficiency. More recently, Lean product development-LPD, validated some aspects of CE (e.g., overlapping of phases), but proposed a more structured way of reducing non-value added activities. The main objective of this study is to discuss the idea that thus far NPD research has mostly focused on efficiency - eliminating waste, reducing time-to-market and costs. This study also discusses the need for an emphasis on creativity in NPD to enhance value creation. In terms of organization, this study contains a literature review on CE, LPD, and group creativity. This study also proposes an abstract model combining concurrent and lean product development aiming at enabling both creativity and efficiency, consequently enhancing value creation. Lastly, limitations and opportunities for future research are proposed.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".