Agile Stage‐Gate Management (ASGM) for physical products
Bibliographic record
Abstract
We present a qualitative study of Agile Stage‐Gate Management (ASGM),: a hybrid new product development methodology that combines Agile and Stage‐Gate Management (SGM) approaches for the coordination of new product development. When applied to software projects, Agile is expected to deliver reduced development times, improved resource utilization, and greater financial success. We examine whether ASGM practitioners realize similar outcomes in a sample of global firms developing complex electro‐mechanical products (e.g., automobile components, railway propulsion systems, and medical devices). Our grounded theory approach articulates an understanding of ASGM through extensive interviews of experienced professionals. Our thematic analysis supports many expected benefits (i.e., speed to market, innovation enabling), but also does not encourage others, and reveals new pitfalls that deserve recognition (i.e., resource inefficiency). ASGM is not a panacea for all product development. Overall, physical product firms adopting this method can expect reduced development times and higher levels of innovation but will expend more resources to complete development projects, but a dichotomy exists. Physical product developers using ASGM experience a negative impact on project resource efficiency due to the need for dedicated resources, frequent product demonstrations, and duplicative management structures.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".