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© 1999, Ellen Domb. This article was first published in Izobretenia, The Journal of the Altshuller Institute, October, 1999.
Key words: Technology forecasting, technology evolution, TRIZ, process improvement, product development
TRIZ (The acronym in Russian for “Theory of Inventive Problem Solving” was introduced to the engineering and product development communities outside the former USSR beginning in the mid-1980’s, with the emigration of some TRIZ practitioners and the availability of the first translations. (Ref. 1.) Early versions of some of the software tools attracted interest in the early 90’s, and in the late 90’s we are seeing rapid spread of awareness of TRIZ in technical communities, as measured by the publications and meetings, and the inclusion of TRIZ in the agendas of the Project Management Institute, the International Congress on the Management of Engineering Technology, the Quality Function Deployment Symposium, the Total Product Development Symposium, the Society of Automotive Engineers, the Institute for Mechanical Engineering (UK), World Quality Day (Finland), etc., as well as the growth of TRIZ specialty meetings in the US and in Europe(Ref. 2).
Much of the early emphasis in TRIZ in the West has been on problem solving, replicating the history of the development of TRIZ in the former USSR. (Ref. 3, 4.) Although the patterns of evolution (or the “Laws” and “lines” of evolution, depending on the translation used—see Ref. 5) were recognized for their power, they were principally used as adjunct problem solving tools. This is “tactical” TRIZ—it is used to improve one product or process.
“Strategic” TRIZ is the use of TRIZ methods to change a product line, a company’s long-term business plan, or the direction of an industry. We are now beginning to see strategic methods derived from TRIZ technology forecasting, which have been given names such as Guided Development (Ref. 6), and Directed Evolution (Ref. 3) and other changes in the TRIZ patterns of evolution (Ref. 7) in the English-language TRIZ literature. Since TRIZ is a technical system, these changes should come as no surprise, and, in fact, should fit the TRIZ patterns of evolution. Preliminary attempts to fit the changes in TRIZ to the S-Curve (or technology maturity curve, as shown in Figure 1.) have been made (Ref. 3) but there has been no agreement on what function or functional parameter should be measured.
These new methods use the same fundamental research on the world collection of patents that is the basis for much of TRIZ, but propose different methodologies for the use of the data. Each of these methods will need to be tested and validated. The methods of experimental science have been used to test each of the additions to TRIZ; that is, the new method is proposed, a number of TRIZ practioners test the new methods against a variety of cases, and, if the new method proves better than the old, it is adopted. (This is the method that was used as each new version of ARIZ was introduced, per Ref. 3) “Better” has been defined as more reliable, more reproducible (different practioners all get the same result) easier to use, producing results that the client likes, etc. As with any experimental science that relies on case studies, there is no one moment at which one can say that a new method has been proven, but as a preponderance of evidence accumulates, practioners will move to using the new methods, and teachers will start teaching it, and it will become the mainstream method.
Figure 1. Derived from Ref. 1. This shows the situation where the new technology is superior in the measured functional capability from its first introduction. Examples would include the clarity of digital cellular phones compared to analog cellular phones, or the number of colors represented by color television compared to black and white. In other situations, the new technology is inferior to the old (clarity of a color TV picture relative to a black and white picture, etc.) and the second curve starts below the first. Some researchers have attempted to show the progress of TRIZ itself on such a graph (Ref. 3) but the measured function that is improving has not been quantified.
The general method of TRIZ technology forecasting is as follows:
Formulate the Ideal Final Result.
Analyze the history of the system. Construct the S-curves for all important functions.
Apply the Patterns of Evolution and the Lines of Evolution to forecast system changes. Depending on which references and translations you use, there are 8 or 11 patterns of evolution, and 230-340 lines of evolution. In the Directed Evolution method (Ref. 3) this step includes assessment of steps that were skipped in the history of the system, and deciding whether to explore the alternatives that the skipped steps would open up.
Formulate the problems that must be solved to achieve the changes to reach the Ideal Final Result, my means of the Lines of Evolution that best fit the situation. (Include failure prevention, reliability, robustness, etc.)
Solve the problems using TRIZ.
Select the development to be implemented based on business decision criteria.
Recent case studies have shown that the correlation between the S-curves for functional capability (Step 2), number of inventions, and level of inventions first demonstrated by Altshuller (Ref. 1) continue to be validated in a wide variety of technologies. The following papers give extensive data and reviews of several methods of gathering the data and assessing the level of inventions:
Ref. 8. Ellen Domb. Automobile airbag technology (The airbag, the sensor, the ignition, and the gas generation systems)
Ref. 9: Michael Slocum. Hermetic sealing technology and self-heating food container technology
Ref. 10: Nathan Gibson. Ultrasonic welding technology
Ref. 11: Darrell Mann: Refrigeration technology
These correlations are shown in Fig. 2. They are a practical tool that many companies are using to assess the maturity of their technologies.
These maturity assessments can be essential for major strategic decisions on the future of a product or a product line. Failure to recognize the onset of maturity can lead to failure to invest in new technologies, and continuation of the attempts to get more from a system that has reached its limit. Likewise, jumping from birth stage to a new curve can forgo the process and product improvements and increased market of the growth stage.
Figure 2. The correlations between the Functional Capability, Level of Innovation, and Number of Innovations, first observed by Altshuller. (Ref. 1) Altshuller’s curve for profitability is not included here, since different industries each have different curves for profitability vs. time.
My own observation in companies in numerous industries (truck parts, military software, chemical processing, food packaging, medical devices, cleaning products, etc.) is that people will do an initial assessment of the maturity of their products based on the current emotional state of the people working on the product. If the people are excited, they will place their product in the “growth” stage. If they are frustrated, they will place it in the “maturity” stage. They will only have a clear picture of the state of maturity after they do the hard work of finding the data in their own records and in the publications of their industry.
Many organizations have begun to see strategic value in supply chain management; that is, the integration of all their suppliers and their customers into a continuous stream of value added processes. (Refs. 12, 13, 14, 15.) TRIZ technology forecasting methods can be used in conjunction with supply chain management as follows:
Evaluate your organization’s key technologies using the methods cited above
Where parts, processes, or subsystems are supplied by outside firms, work with them to evaluate the technology maturity, and the probable future paths of those technologies.
Decide if the suppliers are capable of carrying out the strategic plan. If they are, continue to work with them on technology evolution. If not, decide whether to invest in them (either financially or technologically or both) to make them capable, or whether to seek other means (other suppliers, internal sources, etc.)
This strategic use of TRIZ is in its infancy, and there are no published case studies, since the companies that are using it are deriving considerable proprietary advantage from it. This method should be subjected to the same experimental tests suggested above, to see if it is successful for practioners using TRIZ methods in many industries.
Similarly, TRIZ technology forecasting assessment of the customers’ technology can be used both tactically, to decide when to introduce a product or process so that the customer will be ready to receive it (Ref. 16) or strategically, to decide to market the product to non-traditional customers. (Ref. 17) In both of these situations, the power of TRIZ is that it provides a means for quantifying the decisions that earlier strategy and marketing methods treated intuitively.
The TRIZ technology forecasting methods are used for tactical and strategic decision making. The methods of application are evolving rapidly. The original methods have stood the test of time and the tests of application to new industries that were not included in the original data base. They are also proving themselves in application to supplier and customer technologies. The new methods will require similar extensive testing to discover their benefits and their limits.
G. S. Altshuller, Creativity as an Exact Science. Translated by Anthony Williams. (NY, Gordon & Breach,1988)
See the Calendar of The TRIZ Journal.
TRIZ in Progress. Ideation International, 1999. Section 3 and Appendices 18 and 19. Some of this material was presented in a tutorial by Dana Clark and a paper by Alla Zusman at the Altshuller Institute TRIZCON99.
Ellen Domb. “How to teach TRIZ to Beginners.” Proceedings of the Invention Machine Users Group, 1997. See also the Invention Machine IMLab 1.4, IMLab 2.11 and TechOptimizer 3.01.
Tools of Classical TRIZ,. Ideation International, 1999.
Victor R. Fey and Eugene I. Rivin. “Guided Technology Evolution (TRIZ Technology Forecasting.) The TRIZ Journal, January, 1999.
Victor Fey. “Dilemma of a Radical Innovation: A New View on the Law of Transition to a Micro-Level.” The TRIZ Journal, April, 1999. First published in the Proceedings of the Altshuller Institute TRIZCON99.
Ellen Domb, “Technology Forecasting” Proceedings of the Auto and Airbag Industry Summit, September, 1997.
Michael Slocum, “Technology Maturity using S-curve Descriptors.” Hermetic Sealing case study, The TRIZ Journal, Dec., 1998. Self-heating technology case study, The TRIZ Journal, April, 1999.
Nathan Gibson. “Determination of the Technological Maturity of Ultrasonic Welding” The TRIZ Journal, July, 1999
Darrell Mann. “Using S-Curves and Trends of Evolution in R&D Strategy Planning.” The TRIZ Journal, July, 1999
Robert Austin. “Ford Motor Co.: Supply Chain Strategy.” Harvard Business Review, April, 1999.
Jeffrey H. Dyer, Dong Sung Cho, Wujin Chu. “Strategic Supplier Segmentation: The Next “Best Practice” in Supply Chain Management.” Harvard Business Review, January, 1998.
Marshall L. Fisher. “What is the Right Supply Chain for Your Products?” Harvard Business Review, Marcy, 1997.
Criteria for the Malcolm Baldrige National Quality Award, 1999. Published by the National Institute for Science and Technology, http://www.nist.gov
Geoffrey Moore. Crossing the Chasm. (NY, HarperCollins, 1991)
Clayton Christensen. The Innovator’s Dilemma. (Boston, Harvard Business School Press, 1997)