Bibliographic record
Abstract
Abstract Lean is not minimizing cost, cycle time, or waste. Lean is maximizing value. In product development (PD)—a process where the systems engineering (SE) of products plays a significant role—sometimes getting lean requires doing more, not less. Providing a preferred combination of technical performance, affordability, and lead time requires a flexible and lean PD process. Value is affected not only by the presence of necessary (value‐adding) activities in the PD process but also by the way those activities work together (as a system) to ensure that they use and produce right information. Lean PD requires the right information in the right place at the right time. The kernel of the idea is based on systems thinking. The value of a system is more than the value of its individual components. Similarly, the value of a process is more than the value of its individual activities. How well the components or activities work together (or fail to do so) makes the difference in value. If all the activities add value, how can we ensure that they work together in such a way that the overall process maximizes its potential value? Lean PD requires the systems engineering of processes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".