Improving TRIZ 40 Inventive Principles Grouping in Redesign Service Approaches
Bibliographic record
Abstract
Over the past decade, different studies have been conducted in order to create or redesign services by using the systematic innovation method known as TRIZ – the theory of inventive problem solving. TRIZ has a range of powerful tools to solve problems and the most widely tool used to identify and solve contradictions in the system are the 40 inventive principles (IPs). Only a few studies have grouped the 40 IPs in terms of service context to overcome the problem of consuming time and effort while the designer endeavors to find the best principle(s) that may help to solve a service problem. This study enhanced and refined the previous grouping of the 40 IPs under five service redesign approaches (SRA): self-service, direct service, pre-service, bundled service and physical service. The methodology used to group these principles was by mapping between the principles hints, which have been developed to interpret the TRIZ principles in service context, and each characteristic of the SRA. A comparison between TRIZ contradiction matrix and proposed grouping for a problem case study has been conducted, and it has demonstrated and verified the feasibility of grouping of the 40 principles according to the SRA.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".