expected outcomes from short-term R&D efforts. Assumptions derived from understanding the environment for a particular technology can be integrated into a quantitative forecast for the industry to use in revenue assessments and investment decisions.
Long-term forecasts are forecasts of the deep future. The deep future is characterized by great uncertainty in how current visions, signposts, and events will evolve and the likelihood of unforeseen advances in technology and its applications. These forecasts are critical because they provide scenarios to help frame long-term strategic planning efforts and decisions and can assist in the development of a portfolio approach to long-term resource allocation.
While long-term forecasts are by nature highly uncertain, they help decision makers think about potential futures, strategic choices, and the ramifications of disruptive technologies.
Modern technological forecasting has only been utilized since the end of WWII. In the last 50 years, technology forecasts have helped decision makers better understand potential technological developments and diffusion paths. The range of forecasting methods has grown and includes rigorous mathematical models, organized opinions such as those produced by the Delphi method, and the creative output of scenarios and war games. While each method has strengths, the committee believes no single method of technology forecasting is fully adequate for addressing the range of issues, challenges, and needs that decision makers face today. Instead, it believes that a combination of methods used in a persistent and open forecasting system will improve the accuracy and usefulness of forecasts.
Adler, M., E. Ziglio, eds. 1996. Gazing into the Oracle: The Delphi Method and Its Application to Social Policy and Public Health. London: Kingsley.
Armstrong, J.S. 2001. Combining forecasts. In J.S. Armstrong, ed., Principles of Forecasting, Boston, Mass.: Kluwer Academic Publishers.
Arthur, Brian W. 1996. Increasing returns and the new world of business. Harvard Business Review (July-August).
Balachandra, Ramaiya. 1980. Technological forecasting: Who does it and how useful is it? Technological Forecasting and Social Change 16: 75-85.
Dalkey, Norman C. 1967. DELPHI. Santa Monica, Calif.: RAND Corporation.
Dalkey, Norman C., Bernice B. Brown, and S.W. Cochran. 1969. The Delphi Method, III: Use of Self-Ratings to Improve Group Estimates. Santa Monica, Calif.: RAND Corporation.
Dennis, Robin L. 1987. Forecasting errors: The importance of the decision-making context. Climatic Change 11: 81-96.
Fey, Victor, and Eugene Rivin. 2005. Innovation on Demand: New Product Development Using TRIZ. New York: Cambridge University Press.
Fisher, J.C., and R.H. Pry. 1970. A simple substitution model of technological change. Technological Forecasting and Social Change 3(1): 75-78.
Gordon, Theodore J., and Olaf Helmer. 1964. Report on a Long-Range Forecasting Study. Santa Monica, Calif.: RAND Corporation.
Graefe, Andreas, and Christof Weinhardt. 2008. Long-term forecasting with prediction markets—A field experiment on applicability and expert confidence. The Journal of Prediction Markets 2(2): 71-91.
Green, Kesten C., and J. Scott Armstrong. 2004. Structured analogies for forecasting. Working Paper 17, Monash University Department of Econometrics and Business Statistics. September 16.
Green, Kesten C., and J. Scott Armstrong. 2007. Structured analogies for forecasting. International Journal of Forecasting 23(3): 365-376.
Howard, Ronald A., and James E. Matheson. 1981. Influence diagrams. Pp. 721-762 in Readings on the Principles and Applications of Decision Analysis, vol. I (1984), Howard, Ronald A., and James E. Matheson, eds., Menlo Park, Calif.: Strategic Decisions Group.