Perspective: The Stage‐Gate<sup>®</sup> Idea‐to‐Launch Process—Update, What's New, and NexGen Systems<sup>*</sup>
Bibliographic record
Abstract
Stage‐Gate has become a popular system for driving new products to market, and the benefits of using such a robust idea‐to‐launch system have been well documented. However, there are many misconceptions and challenges in using Stage‐Gate. First, Stage‐Gate is briefly outlined, noting how the system should work and the structure of both stages and gates. Next, some of the misconceptions about Stage‐Gate—it is not a linear process, nor is it a rigid system—are debunked, and explanations of what Stage‐Gate is and is not are provided. The challenges faced in employing Stage‐Gate are identified, including governance issues, overbureaucratizing the process, and misapplying cost‐cutting systems such as Six Sigma and Lean Manufacturing to product innovation. Solutions are offered, including better governance methods such as “gates with teeth,” clearly defined gatekeepers, and gatekeeper rules of engagement, as well as ways to deal with bureaucracy, including leaner gates. Next‐generation versions of Stage‐Gate are introduced, notably a scalable system (to handle many different types and sizes of projects), as well as even more flexible and adaptable versions of Stage‐Gate achieved via spiral development and simultaneous execution. Additionally, Stage‐Gate now incorporates better decision‐making practices including scorecards, success criteria, self‐managed gates, electronic and virtual gates, and integration with portfolio management. Improved accountability and continuous improvement are now built into Stage‐Gate via a rigorous postlaunch review. Finally, progressive companies are reinventing Stage‐Gate for use with “open innovation,” whereas others are applying the principles of value stream analysis to yield a leaner version of Stage‐Gate.
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.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".