Enhancing creativity for development of automation solutions using OTSM-TRIZ: A systematic case study in agronomic industry
Bibliographic record
Abstract
The article describes a method to stimulate users’ creativity within constraint-based scenarios and OTSM-TRIZ, which allows to define the problems and partial solutions to be solved during the design process in an appropriate manner. The proposed method aims to overcome constraints and problems defined within product development and related organization resources. Indeed, if these constraints are not properly taken into account, the risk of generating unsuccessful and even ineffective solutions can be high. In this work, a method has been defined, based on the OTSM-TRIZ theory: it guides the users toward the problem solution through a mapping of both the problem to solve and the relationships existing among the problems and constraints. A step-by-step approach is used to describe and propose a systematic structure, allowing to link the conceptual solution with specific solution criteria in the automation field. The validation of the proposed method corresponds to a real case study, that is, the necessity of increasing the productivity of an operational plant for the palletizing process has been selected to discuss the method implementation. Finally, the results of the case study were considered successful, because it was not necessary to introduce high investments for solution development and implementation.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".