Problem-Solving in Product Innovation Based on the Cynefin Framework-Aided TRIZ
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
Different problems in the process of product innovation are often caused by external environmental changes of the product. There is a lack of research on classifying the problems associated with product environment changes to aid in applying tools of the Theory of the Solution of Inventive Problems (TRIZ) for problem-solving. This paper proposes a Cynefin framework to classify the problems into disorder, chaotic, complexity, complicated and simple areas according to the external environment changes. Each area of problems is then solved by corresponding design tools in TRIZ. Chaotic and complex problems are converted into complicated or simple areas by the technology evolution and effect search. Complicated or simple areas are combined considering conflicts expressed by an Element-Name-Value (ENV) model. Key conflicts are determined by simplified rules of a node conflict network. A problem-solving methodology in product innovation is proposed based on Cynefin framework-aided TRIZ. The proposed method is applied in the design of an enterprise SJL900/32 mobile bridge erecting machine.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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