Prioritized relevant trend identification for problem solving based on quantitative measures

- Identification of trigger solution with priority. Objectivity, repeatability, knowledge accumulation.
- Quantitative methods for TRIZ solution identification.
- A paradigm shift from the traditional TRIZ qualitative methods.
- Opens up a new research direction of using quantitative measures for TRIZ problem solving.

This study proposed quantitative/mathematical ways to identify TRIZ solution models to given problems based on some similarity measures. The use of quantitative methods allows the users quickly and objectively obtaining solution models to a problem with priority based on existing trends and solved data base which is the accumulation of many expert knowledge and experiences instead of individual expert's judgment.In this paper, the similarity concept was used to determine the relevant evolutionary trends and corresponding stages as solution models. A total of 124 known cases from literature and 10 author's own industrial cases verified the validity of the method in screening in n highest potential solution models for solution generation where n can be determined by the user. By conducting a 4-fold verification of 124 cases, the eight highest similarity solution models provided a hit rate exceeding 92% coverage of the original solutions. This substantially exceeded the hit rate of eight randomly selected solutions, which was less than 5%. Furthermore, problems and suggested trigger solutions were given to 20 TRIZ level 3 practitioners to test the effectiveness of those screened trigger solutions. On average, the eight most similar trigger solutions provided 4.2 effective specific solutions (52.6%) as opposed to that of the eight random trigger solution which is 1.5 effective specific solutions (18.69%). The eight worst similar solutions on average generated 0.86 solutions (10.79%).The ideas can also be extended to identifying prioritized solutions objectively and quickly with any other TRIZ problem solving tools which contain large number of solution models such as effect database, principles, and standards. This establishes a paradigm shift new research direction for TRIZ-based scientific research contributing to TRIZ recognition in scientific fields. The contributions of this study include: (1) Integrating trends from Traditional and Darrell Mann's TRIZ to form a set of 52 trends in which each trend stage's characteristic attributes and evolutionary causes to the stages were completed. (2) A mathematical method was used to develop an objective and repeatable trend identification system in which the hit rate and feasible solutions substantially exceeded randomly selected solutions. (3) Providing a means to continually accumulate expert knowledge and experience by integrating more expert-solved cases to provide users a rapid, objective, and effective problem-solving system. This implies a continuous learning system which uses cumulative knowledge from many experts objectively instead of otherwise knowledge from individual experts. (4) Opening up a new research direction of identifying prioritized solution models using quantitative/mathematical measures instead of traditional qualitative reasoning.