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TRIZ in Retrospect and Prospect

Chien Chiang Lin, Hsing-Hung Lin, Kun-Chih Huang


In last 70 years TRIZ(Theory of Inventive Problem Solving) has been developed prosperously,  including the establishment of associations, training centers, consulting companies, and software suppliers; research projects as well as related outcomes from various domains did enrich the accumulation of the literature. Actually, a plethora of studies could be discovered from different databases extensively tackling related issues of TRIZ from theoretical perspectives, methodological concerns and the combination of TRIZ and other tools. Practically speaking, manufacturing as well as service industries were the major playground for utilizing TRIZ to improve operational performance for achieving excellence. It is, therefore, about the right time to understand the progress of applying TRIZ methodology from various fields in the world and to set a research agenda for future research and application. The authors conducted a systematic review of previous studies selected from several databases. Based on statistical analysis and the results of text/data mining, the current study concluded that the most adopted tools in TRIZ are contradiction and patent analysis; furthermore, quality function deployment (QFD) and green design are the most popular methods used in combination with TRIZ.


TRIZ; Data Mining; Research Trends

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Agrawal, R. and Srikant, R., 1994, Fast Algorithms for Mining Association Rules. Proceedings of the 20th International Conference on Very Large Databases, Santiago. Chile, 487-499, Sep.

Adriaans, P. and Zantinge, D., 1996, Data Mining, Addison-Wesley, Harlow, England.

Altshuller, G., Shulyak, L. and Rodman, S., 1997, 40 Principles: Triz Keys to Technical Innovation, Worcester, MA: Technical Innovation Center, Inc.

Altshuller, G., 2000, The Innovation Algorithm: TRIZ, Systematic Innovation and Technical Creativity, MA: Technical Innovation Center, Inc..

Han, J., Kamber, M. and Pei, J., 2000, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers.

Mann, D., 2002, Hands on SYSTEMATIC INNOVATION, 2nd ed., CREAX Press.

Scheffer, T., 2004, Finding Association Rules That Trade Support Optimally against Confidence. The 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435.

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