Improving new product development innovation effectiveness by using problem solving tools during the conceptual development phase: Integrating Design Thinking and TRIZ
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.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".