Predicting the Future with TRIZ

By Kalevi Rantanen

From the Editors: The practice of creating predictions about the future has been conducted for centuries. Predicting the future is part of Strategic TRIZ – a part of the DMAPI roadmap – and can be utilized to assist in the creation of multi-generation product plans (MGPPs) and/or technology forecasts. Used in this fashion, the application is based upon a set of customer and/or societal needs. Open-ended predictions, like those in this article, have been used to help understand the creative persona.


Many technology forecasts from Jules Verne's novels in the 19th century to Herman Kahn's and Anthony Wiener's scenarios in the 1960s come to pass. Hypothetically, if the patterns of evolution are added to futurology, predictions can be still improved. To test the hypothesis, this paper presents a list of 100 predictions for the years 2007-2040 (comparable to Kahn's and Wiener's list for years 1967-2000), based on journalistic research from 2002-2006.


TRIZ, foresight, prediction, journalism

Significant Statistical Evidence

In The Year 2000, Herman Kahn and Anthony Wiener presented "One Hundred Technical Innovations Very Likely in the Last Third of the Twentieth Century." [7, p. 52] Some forecasts were incredibly accurate.

About 20 innovations had happened by 2000, including:

The remaining 80 predicted innovations either did not happen or the outcome remains unclear. For example:

Some innovations, most notably personal computers, were not predicted. If we subtract lacking innovations from the list, we can crudely conclude, that about 10 percent of the future is predictable.

More than one thorough evaluation studies has been produced – notably in Japan. In 1996, the Japanese Science and Technology Agency evaluated a forecast made in 1970-1971 – 26 percent of 588 topics were realized. A second forecast was made in 1976; of 549 topics 21 percent were realized. [15, p.12] But what do these figures mean? Is ten or twenty percent a good or bad result?

There is much talk of failures. People who deny the possibility of any reasonable predictions often cite Thomas Watson, the founder of IBM, who said, "I think there's a world market for about five computers." The founder of Microsoft, Bill Gates, stated in 1981 that 640K memory "ought to be enough for anybody." Both companies have been successful in business for decades, but every successful person and company has made wrong predictions.

Insurance companies do well, although they cannot predict a single event. There is significant statistical predictability in the world. Ten percent is better than guessing. In the lottery, chances of winning are usually one of millions – as you select from some tens of numbers. In technology there are many more combinations of elements that can form myriads of mixtures, alloys and compounds. Expert knowledge allows for the selection of a limited set of possible futures. One good idea has a better chance than ten bad ones. The rule of thumb is that one successful project compensates ten failures and still provides profit. But, do we want to know the whole future? If 100 percent of the future is predictable, then nothing could be changed – there would be no freedom of choice. The ideal accuracy, therefore, would be somewhere between 0 and 100 percent

There are, then, two conclusions:

  1. Scientific and technical knowledge enables predictions that are reliable enough to improve business and political decision making.
  2. It is not at all necessary to know all the future: we do not want to know the future precisely. Totally predestined future can equate to lack of choice and space for creativity.

While saying that predictions based on expert knowledge are useful, there is still room for improvement:

  1. How much more accurate do the predictions need to be? At the moment the question is academic. We need to focus on improving forecasts. Improving business is an obvious reason to make predictions. If you know 30 percent of the future, while your competitors know 10 or 20 percent, you will win.
  2. Make predictions more specific.

Science and Technology Journalism

A look at journalism demonstrates the previous conclusions. I have written about 140 articles about science and technology in the last four years; nearly all of them contain predictions. A reader of technology news wants to know not only what happened, but also what will happen in the future.

The following are some examples of recent predictions in scientific and technological journalism:

A Hypothesis: Patterns of Evolution Improve Predictions

The accuracy of predictions is an everyday concern. Readers require reliable, balanced and non-biased stories. Traditional predictions are based on knowledge of different sciences and technologies. They are made by one person or by groups of subject matter experts. The most important method for prediction is the Delphi Method, developed by RAND Corporation in the 1960s by one such group. The work can consist of several rounds as experts comment upon and correct eachother's work.

Independent of "mainstream futurology," Genrich Altshuller and his colleagues developed the Theory of Inventive Problem Solving, TRIZ. TRIZ experts claim that there are laws, trends, patterns and evolution lines in technology and that there are tools that be used for technical forecasting.

But can you prove whether tools like the Delphi Method and TRIZ add value to forecasting?

Genrich Altshuller and Rafael Shapiro wrote of "basic trends of technology evolution" in the first paper about TRIZ. [1] In 1979, Altshuller defined and described eight laws and their use with forecasting innovations. For example, a heat-resistant diving suit for mining rescue teams predicted in 1949 was realized 20 years later. [2, pp. 215-216]. Also, Altshuller suggested developing engineering systems "without waiting until the problem arises." [2, p. 45]. Today, we frequently try to act proactively.

In And Suddenly the Inventor Appeared, Altshuller says, "We understand the logic of technical systems evolution and can foresee the arising of new problems, knowing beforehand how they can be solved." [3, p.60]

Nearly all TRIZ case studies contain, plainly or implicitly, predictions. Recent examples include:

However, while there are many examples of forecasting, statistical evidence remains scarce.

100 Predictions with TRIZ

There have not been packages or lists of predictions, allowing making statistically reliable conclusions. If a "TRIZ prediction" hits the mark, does it mean that the theory works? Not necessarily, since there may be ten other predictions that miss the mark. If a TRIZ prediction fails, does it mean that the tools of TRIZ are bad? Again, not necessarily, since at the same time these tools can provide ten or twenty successes. We need more than a few predictions to prove the methodology. I used TRIZ to forecast the following 100 innovations likely to come in the next 33 years – by the year 2040.









Housing and Buildings










Sport and Entertainment




This list is only a partial picture of the future and nearly everyone will disagree with one or more predictions. I encourage readers to publish improved lists.

Conclusion: Predictions Help Validate TRIZ

The potential benefits of predictions have been largely ignored. Forecasting is a good way to test and verify theories of problem solving, particularly TRIZ. TRIZ is considered science and Altshuller spoke of the "exact science" of creativity.

One requirement of scientific research is that theories should be empirically tested. Empirical evidence of the validity of TRIZ consists mainly of success stories, references and testimonials. This evidence is necessary, but not enough. But, another feature of science is its ability to predict the future. Science must give significantly more precise forecasts than pure guesswork. A new theory, like TRIZ, should give significantly more precise forecast than traditional approaches and also should be applicable to practical forecasting, research and development work. If both hypotheses and results are kept private as proprietary information, science evolves slowly. Publication allows for a rigorous verification. When hypotheses are publicized, it is not possible to fit facts to a result after the fact.

Making predictions is a good way to test TRIZ. If the theory is valid, predictions made by TRIZ should be significantly more precise than forecasts made by the Delphi Method or other conventional tools alone.

Extra: The Predictions for Comparison

To more easily compare these predictions with Kahn's and Wiener's list, the foresights are presented here as a single list:

  1. Zero vision realized in transportation make crashes rare
  2. Automated people movers that combine the benefits of public and private transport
  3. Automated goods movers
  4. Mass-customized and easily constructed roads
  5. Personal flying devices
  6. "Wingships," combining the benefits of the airplane and the ship
  7. Space elevators
  8. Micro-spacecrafts
  9. Super-sonic airplanes widely used
  10. Miniature, solar-powered automatic airplanes and helicopters
  11. "Smart" and automated cars
  12. Shared use of vehicles
  13. Totally networked cars with TVs, phones and computers
  14. Rise of nuclear, solar and hydrogen energy; decrease of oil consumption
  15. Use of small nuclear reactors and miniature atomic batteries
  16. Safer nuclear reactors
  17. Use of fusion power starts
  18. Mining helium on the Moon for fusion reactors on Earth
  19. Functional food or food with health effects
  20. Smart food packages
  21. Conservation agriculture widely practiced
  22. Artificial meat
  23. Automated cooking
  24. Mass-customization in clothing
  25. Self-cleaning clothing
  26. Clothing that collects and releases heat
  27. Smart clothing with embedded phones and computers
  28. Clothing capable of changing size
  29. Clothing capable of changing color
  30. Elevators in all buildings
  31. Accessible houses and barrier-free environments
  32. Domes and other curved parts in buildings widely used
  33. Storm safe buildings
  34. Earthquake safe buildings
  35. Floating homes and cities
  36. Walls that change transparency and color
  37. Multi-store cities with elevated and underground streets
  38. Mass-customized housing
  39. Automated erection of buildings and wide use of construction robots
  40. Flexible shells and thin films widely used in buildings
  41. Mesh networks of very small smart sensors of "smart dust"
  42. Multimodal interfaces using voice, video and haptic interface
  43. Reprintable electronic office paper for computers
  44. Ubiquitous video conference technology and mobile video phones
  45. 3D-TV ubiquitous
  46. Tele-immersion (TV around the user)
  47. Good voice recognition and voice-to-text transformation
  48. First online interpreters
  49. Everyone has thousands of very small computers
  50. Simple and cheap, disposable and recyclable phones and computers
  51. Advanced telepresence
  52. Distance interpretation via portable video phones
  53. Advanced avatars or digital personalities
  54. "World library" provides access to all audiovisual information
  55. Brain-computer interfaces
  56. Intuitive interfaces to guess the needs of the user
  57. Biometric recognition instead of paper documents
  58. Virtual shopping
  59. Electronic books
  60. Individual GPS
  61. Life recording
  62. Robots making robots
  63. Swarms of many small robots
  64. Robotization of agriculture
  65. Home robots for cleaning, gardening and other tasks
  66. Patrol robot for public and private security
  67. Robots, vehicles and buildings that can assemble themselves
  68. Distance control of health
  69. Health monitors implanted into the body
  70. Predictive genetic tests
  71. Individually tailored drugs
  72. Surgeon-robots
  73. Vaccination against tooth decay
  74. Manufacturing parts of human body
  75. Devices and drugs for the control of obesity
  76. Easily available technologies for cosmetic surgery and other ways of reshaping the human body
  77. Smart toilets monitor health
  78. Chip-assisted human memory or neural prosthetics
  79. First steps of bionics and cyborg technology
  80. Technology to change the color of skin
  81. Economically viable ways to collect, store and use carbon dioxide
  82. Effective ways to prevent dust and collecting dust
  83. Improved acoustic environments and noise cancellation
  84. Biological and other micro-level control of oil pollution
  85. Limited control of climate in cities and small biospheres
  86. Production of fresh water
  87. Cyborg athletes containing artificial muscles
  88. Space sports and holidays in space
  89. Robots playing football and other games challenge human athletes
  90. Improved accuracy of lie detectors
  91. Electronic money instead of cash
  92. Desktop production plants
  93. Automated stores
  94. First steps of improved animal intelligence
  95. Prisons without walls or electronic surveillance of prisoners
  96. Biosensors widely used
  97. Wide use of photonics
  98. Use of photocromic materials
  99. Nanomaterials, as cheap as steel and many times stronger
  100. Artificial photosynthesis


  1. Altshuller, G., Shapiro, R., Psychology of Inventive Creativity, Izobretenia vol. II, 2000, 23-27.
  2. Altshuller, G., Creativity as an Exact Science, Gordon and Breach, New York, 1984.
  3. Altshuller, G., And Suddenly the Inventor Appeared, Technical Innovation Center, 1996.
  4. Chuksin, P., Shapakovsky, N., "Information Analysis and Presentation in Forecasting," The TRIZ Journal, March 2006.
  5. Domb, E., "Strategic TRIZ and Tactical TRIZ: Using the Technology Evolution Tools," The TRIZ Journal, January 1996.
  6. Fey, V., Rivin, E., Guided Technology Evolution (TRIZ Technology Forecasting), Izobretenia vol. 1, 1999, 11-19.
  7. Kahn, H., Wiener, A., The Year 2000, The Macmillan Company, New York,1967.
  8. Kowalick, J., "Technology Forecasting Using TRIZ," The TRIZ Journal, January 1996.
  9. Petrov, V., "The Laws of System Evolution," The TRIZ Journal, March 2002.
  10. Rantanen, K., Domb, E., Simplified TRIZ: New Problem Solving Applications for Engineers & Manufacturing Professionals, CRC St. Lucie Press, Boca Raton FL USA, 2002.
  11. Rantanen, K., "Homes for Strong Families, Children, Seniors and All Others. Universal Design, Design for All and Forty Principles of TRIZ Enforce Each Other," The TRIZ Journal, May 2005.
  12. Salamatov, Y., TRIZ: The Right Solution at the Right Time, Insytec, Hattem, 1999.
  13. Salamatov, Y., "Memorandum On New Field For TRIZ Application And Development: TRIZ In Virtual World (Business Proposal For Years 2005-2020)," The TRIZ Journal, July 2005.
  14. Savransky, S., Engineering of Creativity, CRC Press, Boca Raton, 2000.
  15. Seya, M., "Tecnology Foresight in Japan," International Seminar Foresight Studies on Science and Technology: International Experiences, Brasilia, Brazil, September 27, 2000.
  16. Teplitskiy, A., Roustem, K., "Evolution Trends in Nuclear Soil Logging Tools," The TRIZ Journal, November 2006.
  17. Tompkins, M., Price, T., Clapp, T., Parker, I. "Technology Forecasting of CCD and CMOS Digital Imaging Technology using TRIZ," The TRIZ Journal, March 2006.
  18. Weitzenböck, J., Marion, S., "Using TRIZ to Develop New Corrosion Protection Concepts in Shipbuilding – A Case Study," The TRIZ Journal, December 2006.

About the Author:

Kalevi Rantanen worked in Finnish youth organizations, primarily on problems of education, in the 1970s. From 1979-1985, he studied in the former USSR and earned his M.Sc in mechanical engineering and was introduced to TRIZ. Rantanen worked in Finnish industry until 1991, while also a TRIZ trainer. Since 1991, he has been an independent entrepreneur and has concentrated on science and technology journalism since 2002. Contact Kalevi Rantanen at kalevi.rantanen (at)

Copyright © 2006-2011 –, CTQ Media. All Rights Reserved
Reproduction Without Permission Is Strictly Prohibited – Request Permission

Publish an Article: Do you have a innovation tip, learning or case study?
Share it with the largest community of Innovation professionals, and be recognized by your peers.
It's a great way to promote your expertise and/or build your resume. Read more about submitting an article.