A New Philosophy of R&D Management: Combining Third-Generation R&D Management With Technology Road Mapping (TRM)
Bibliographic record
Abstract
Abstract This paper presents a new philosophy of research and development (R&D). it is combined the mode of third generation R&D management (Philip A. Roussel, Kamal N.Saad and Tamara J. Erickson, 1991) with technology road maping (TRM) (Jonah M. Duckles, and Edward J. Coyle, 2002, Alain Leger, Fausto Giunchilia, Ana V.Zhdanova and Niana Maynard, 2005). Main management system of the third generation R&D management and its functions, advantages and application of TRM of petroleum technology R&D were described in the paper. At the same time, milestone's elements of technology R&D and RTM's construction are discussed. This paper also point out how to keep company business target in accordance with the technology development target through identifying and prioritizing technology investment decisions, and repositioning company technology capabilities. The visions of the future technology scenario planning and translating the TRM to a technology strategy can be put forward. The methodologies, tools and templates of TRM are also presented in the paper, prioritizing technology capability and upgrading and sharing the technology future. Therefore, a chain was established among the technologies, products and services development plans, and linking of technologies to business drivers and strategy targets in order to reduce the risk of R&D of the technology. Finally, the paper proposes formulation of the TRM, and it also emphasizes that the RTM is an excellent management tool for analyzing and prioritizing potential future technology acquisition. At present, this method has been implemented to the R&D management in PetroChina.
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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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".