Improving new product development innovation effectiveness by using problem solving tools during the conceptual development phase: Integrating Design Thinking and 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
The objective of this research is to improve innovation effectiveness during new product development (NPD) processes in industry by using problem‐solving techniques during the conceptual development phase. The concept phase of physical NPDs is widely recognized in the literature as the time when the target market is identified, alternative product concepts are created and evaluated for further development and testing, also called the “fuzzy front end” or “discovery stage”. Design Thinking (DT) and TRIZ were the chosen problem‐solving techniques to support this stage because of their complementariness. While DT is most recognized as an approach that drives project teams toward the end‐users, TRIZ has its main strength during idea generation and selection processes where it has a robust set of analytical tools to drive NPD teams to a final product concept. After conducting a literature review to understand the strengths and limitations of both techniques, a framework is proposed by integrating them into the conceptual development phase of an industrial NPD process. The proposed framework is then tested and validated after being applied successfully in an NPD process in the automotive industry. The automotive industry is a good example of an incremental type of industry when designing its components for new vehicle models, and is therefore a very appropriate laboratory for validating the proposed framework.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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