By Michael S. Slocum and Catherine O. Lundberg

Technology innovation can be forecast emotionally and empirically. Getting emotionally involved with a product or process can help in the initial growth stage, but then serve as a barrier when negative emotions (e.g., frustration) inevitably arise. Moving to data-based processes of forecasting prevent such emotional challenges. A previous article focuses on eight (non-TRIZ) forecasting methodologies. This article describes a case study in which TRIZ-based forecasting methods to successfully develop a product.

TRIZ, the Russian acronym for Theory of Inventive Problem Solving, is emerging as a powerful scientific tool that helps decision-makers to make strategic forecasting decisions. Assessment of a company’s current technology should drive the direction of the R&D planning process.

TRIZ Technology Forecasting

Russian scientist Genrich Altshuller, along with his colleagues, found that any system evolves in a biological pattern, meaning that it will go through four main stages also known as: infancy, growth, maturity and decline. These stages are plotted on the biological “S-curve” shown in Figures 1 and 2.

Figure 1: A System’s Evolution [1, 2, 5, 6, 7]

Figure 2: The Main Stages of System Evolution

Three main descriptors are used to assess the life cycle stage (or technological maturity) of a technological system on its S-curve:

1. Number of patents per time period
2. Level of innovation per time period
3. Technical performance per time period

Each descriptor has a characteristic profile or shape as shown in Figure 3.

Figure 3: Descriptor Characteristic Profiles [5]

The company can collect data to construct each of the descriptor curves. The shapes of each of the descriptor curves are compared to the shapes of the characteristic curves. A composite analysis of the three curves provides a data-driven assessment of the maturity of the company’s technological system.

Other descriptors are sometime used to refine the maturity of a system such as cost reduction-related inventions that relate to making the product cheaper – such as improvements to manufacturing technology or method of assembly. The number of such inventions tends to increase as the system matures. (See Figure 3.)

Patterns of Evolution

Patterns of evolution represent a compilation of trends that document strong, historically recurring tendencies in the development of man-made or natural systems (seen in the intellectual literature or the historical evolution of products). The eight fundamental trends are the main tools for technology forecasting.

Altshuller identified eight original trends:

1. biological evolution
2. increasing ideality
3. evolution toward dynamization and controllability
4. complexity-simplicity
5. evolution with matching and mismatching elements
6. non-uniform development
7. evolution toward micro-level and the use of field
8. decrease human involvement [8, 9, 10, 11]

Systematically applying the patterns of evolution to a company’s technological system results in a number of possible solution paths. The solutions, or directions, recommended by one trend are not unique – they often overlap. Once a company has generated multiple solution paths, management decisions can be made to develop the R&D plan for the company.

Self-heating container technology (see Figure 4) has had patent activity starting from 1976. The patent activity during the ensuing 23 years has been varied and non-consistent. These particularities make self-heating technology an ideal candidate for maturity mapping. Data was collected relevant to the technology that would provide necessary information to create the required graphs represented in Figure 3.

The patent search criteria was devisedusing Boolean selected to ensure capture of relevant and associated self-heating technologies. The abstracts were then reviewed for relevancy and the search-returned patent database was modified accordingly. A properly constructed search can reduce the abstract review period while a search criterion that is too broad in nature significantly increases the abstract review period.

Figure 4: OntroSelf-heating Container

Figure 5: The Ontro Self-heating Container: (l-r) section showing body and cone, metal end for the bottom of the container, metal end for the top of the container, draining of the water onto the CaO and energy release from the cone to the beverage.

Performance

Sustained core temperature was selected as the primary performance characteristic to trend. Data concerning sustained core temperature was collected from literature and company specifications for the last twenty years. This data was plotted as Figure 6. The core temperature sustainment is measured in seconds beginning when the specified core temperature is reached and ends when the specified temperature is no longer maintained due to exothermic exhaustion (the nominal required temperature is monitored and considered attained to +/- 5 degrees Fahrenheit). A portion of the energy created is required to thermally condition internal membranes and is therefore not directly translated to the temperature elevation of the beverage. Due to this, beverage temperature was also selected as a primary performance characteristic. Beverage temperature would be the final quality criteria performance as it is directly transferred to the customer. (See Figure 7.)

Figure 6: Sustained Core Temperature vs. Year [5]

Figure 7: Beverage Temperature vs. Year (There are noticeable differences between core temperature (Figure 6) and beverage temperature. [5]

Number of Inventions

The number of self-heating technology patents was collected from a patent database and these figures were collected. (See Figure 8.)

Figure 8: Patents Per Year for Self-heating Technology (Also includesrelevant criteria not specifically naming “self-heating”; Reproduced from Invention Machine TOPE 3.0 chart.)

Level of Innovativeness

The associated aggregate level of inventiveness for the self-heating technology patents are plotted in Figure 10. The criteria used for level determination was primarily a combination of the following categories and the individual patent ranking versus each category. The following category list was used:

• Required trial and error iterations (if known or surmised (acknowledge strength or weakness of any assumption(s))
• Presence or absence or invisibility of a contradiction(s) (administrative, technical or physical)
• Number of contradictions
• Strength of the contradiction(s)
• Impact on the relevant field
• Impact on science
• Degree of system change

Figure 9: Highest Level of Inventiveness Per Year [5]

Conclusion

The stage indicators placed the existing self-heating technology in the infancy stage. (This is demonstrated by superimposing the predicted curves from Figures 1 and 2 over the experimental data from Figures 6, 7, 8 and 9.) In each case, the correlation suggests an immature status. A clear strategic implication was realized: invest in the production and marketing for this technology. Previous technologies were employed, but the peak core and beverage temperatures realized had been inadequate due to secondary limitations (or problems) associated with the technology in question (e.g., cost to manufacture, weight, safety). Therefore, several S-curves were initiated but each declined prior to the emergence of the technology.

This is the first cycle of this technology that has emerged. Several secondary problems were resolved and innovative design and utility patents were filed to protect this technology as it matures. Maturity mapping will be used to ensure the growth of this technology is understood and strategically managed to maximize profitability. Resources will be directed to a superseding technology at a point conducive to the maintenance of a positively sloped profitability curve.

References

1. Gahide, Severine, Slocum, Dr. Michael S., and Clapp, Dr. Timothy G.,”Application of TRIZ to Technology Forecasting – Case Study: Yarn Spinning Technology,”The TRIZ Journal, July 2000.
2. Gibson, Nathan, Slocum, Dr. Michael S., and Clapp, Dr. Timothy G, “The Determination of the Technological Maturity of Ultrasonic Welding,” TheTRIZ Journal, July 1999.
3. Heath, Darren, Slocum, Dr. Michael S., and Clapp, Dr. Timothy G.,”Addressing Salt Issues in Textile Dyeing Using an ISQ and ARIZ,” The TRIZ Journal, January 2000.
4. Mann, Darrell and Domb, Ph.D, Ellen, “Business Contradictions – 1) ‘Mass Customization,'”The TRIZ Journal, December 1999.
5. Slocum, Ph.D, Michael,”Technology Maturity Using S-curve Descriptors,” The TRIZ Journal, April 1999.
6. Slocum, Ph.D, Michael, Vijayakumar, Sanjana and Clapp, Dr. Timothy G., “Maturity Mapping of DVD Technology,” The TRIZ Journal, September 1999.
7. Slocum, Ph.D, Michael, “Technology Maturity Using S-curve Descriptors,” The TRIZ Journal, December 1998.
8. Altshuller, G.S.,Creativity as an Exact Science, Gordon and Breach Publishers, 1984.
9. Savransky, Semyon D.,Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving, CRC Press LLC, 2000.
10. Terninko, John, Zusman, Alla and Zlotin, Boris, Systematic Innovation: An Introduction to TRIZ, St. Lucie Press, 1998.
11. TE589A, Theory of Inventive Problem Solving, Graduate Class at North Carolina State University, Slocum, Dr. Michael S., and Clapp, Dr. Timothy G.

About the Authors:

Michael S. Slocum, Ph.D., is the principal and chief executive officer of The Inventioneering Company. Contact Michael S. Slocum at michael (at) inventioneeringco.com or visit http://www.inventioneeringco.com.

Catherine O. Lundberg is the director of operations of OnTech Operations Inc. She has seven years of experience utilizing customer requirements to drive new product development, manufacturing design, product enhancement, cost and waste reduction, and process improvement. Lundberg’s focus is on incorporating high level technologies and science with basic consumer interaction to produce products with exceptional customer acceptance. Contact Catherine O. Lundberg at catherine.lundberg (at) ontech.com.