Simulation of the New Product Development Process for Performance Improvement
Bibliographic record
Abstract
This paper explores the linkages between key features of the new product development (NPD) process and NPD performance and suggests ways of designing the process to improve performance. Using a stochastic computer model, we examine, under varying uncertainty conditions, how the key features of overlapping and functional interaction affect the performance measures of development time and effort (total person-days for a project). Findings indicate that, first and foremost, whether or not overlapping occurs, increasing functional interaction eventually leads to a trade-off between development time and effort. Second, an “early-start-in-the-dark” approach of increasing overlapping with no functional interaction is inferior even to an “over-the-wall” approach. Third, increasing overlapping when some functional interaction exists is beneficial in low uncertainty and harmful in high uncertainty. Fourth, concurrent engineering (CE) is appropriate under low uncertainty, while a type of sequential engineering (SE), different than the “over-the-wall” approach, should be used under high uncertainty, and last, dedicated teams are suitable under high, and not low, uncertainty. We developed the model with the aid of a company and validated it against a published account of five case studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".