Appraisal of New Product Development Success Indicators in the Aerospace Industry
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
Assessing performance in developing new aerospace products is essential. However, choosing an accurate set of success indicators to measure the performance of complex products is a nontrivial task. Moreover, the most useful success indicators can change over the life of the product; therefore, different metrics need to be used at different phases of the product lifecycle (PLC). This paper describes the research undertaken to determine success measurement metrics for new product development (NPD) processes. The goal of this research was to ascertain an appropriate set of metrics used by aerospace companies for assessing success during different phases of the PLC. Furthermore, an evaluation of the differences and similarities of NPD success measurement was carried out between aerospace companies and the nonaerospace companies practicing in the business-to-business (B2B) market. Practical case studies were carried out for 16 Canadian and Danish companies. Seven companies belong to the aerospace sector, while nine are nonaerospace companies that are in the B2B market. The data were gathered from relevant product managers at participating companies. The outcomes of this research indicate that: (1) the measurement of success of aerospace NPD practices depends on the PLC phase being measured, (2) product and process management performance are the more important indicators of success in the early PLC phases with revenue and market share indicators being important during late phases, and (3) there are reasonable similarities in success measurement between aerospace and nonaerospace B2B companies. Sets of metrics for measuring success during each PLC phase of aerospace products are proposed, which can guide companies in determining their ideal practices.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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