In 1945, a report called Toward New Horizons was created for the U.S. Army Air Forces (von Karman, 1945). This report surveyed the technological development resulting from WWII, discussed the implications of that development, and suggested future R&D (Neufeld et al., 1997). Toward New Horizons, written by a committee chaired by Theodore von Karman, arguably represents the beginning of modern technology forecasting.

In the late 1940s, the RAND Corporation was created to assist the Air Force with, among other things, technology forecasting. In the 1950s and 1960s, RAND developed the Delphi method to address some of the weaknesses of the judgment-based forecasting methodologies of that time, which were based on the opinions of a panel of experts. The Delphi method offers a modified structured process for collecting and distilling the knowledge from a group of experts by means of a series of questionnaires interspersed with controlled opinion feedback (Adler and Ziglio, 1996). The development of the Delphi method marked an important point in the evolution of technology forecasting because it improved the value of an entire generation of forecasts (Linstone and Turoff, 1975). The Delphi method is still widely used today.

The use of technology forecasting in the private sector began to increase markedly during the 1960s and 1970s (Balachandra, 1980). It seems likely that the growing adoption of technology forecasting in the private sector, as well as in government agencies outside the military, helped to diversify the application of forecasts as well as the methodologies utilized for developing the forecasts. The advent of more powerful computer hardware and software enabled the processing of larger data sets and facilitated the use of forecasting methodologies that rely on data analysis (Martino, 1999). The development of the Internet and networking in general has also expanded the amount of data available to forecasters and improved the ease of accessing these data. Today, technology forecasting continues to evolve as new techniques and applications are developed and traditional techniques are improved. These newer techniques and applications are looked at later in this chapter.

DEFINING AND MEASURING SUCCESS IN TECHNOLOGY FORECASTING

Some would argue that a good forecast is an accurate forecast. The unfortunate downside of this argument (point of view) is that it is not possible to know whether a given forecast is accurate a priori unless it states something already known. Accuracy, although obviously desirable, is not necessarily required for a successful forecast. A better measure of success is the actionability of the conclusions generated by the forecast in the same way as its content is not as important as what decision makers do with that content.

Since the purpose of a technology forecast is to aid in decision making, a forecast may be valuable simply if it leads to a more informed and, possibly, better decision. A forecast could lead to decisions that reduce future surprise, but it could also inspire the organization to make decisions that have better outcomes—for instance, to optimize its investment strategy, to pursue a specific line of research, or to change policies to better prepare for the future. A forecast is valuable and successful if the outcome of the decisions based on it is better than if there had been no forecast (Vanston, 2003). Of course, as with assessing accuracy, there is no way to know whether a decision was good without the benefit of historical perspective. This alone necessitates taking great care in the preparation of the forecast, so that decision makers can have confidence in the forecasting methodology and the implementation of its results.

The development of a technology forecast can be divided into three separate actions:

  • Framing the problem and defining the desired outcome of the forecast,

  • Gathering and analyzing the data using a variety of methodologies, and

  • Interpreting the results and assembling the forecast from the available information.

Framing the problem concisely is the first step in generating a forecast. This has taken the form of a question to be answered. For example, a long-range Delphi forecast reported by RAND in 1964 asked participants to list scientific breakthroughs they regarded as both urgently needed and feasible within the next 50 years (Gordon and Helmer, 1964).

In addition to devising a well-defined statement of task, it is also important to ensure that all concerned parties understand what the ultimate outcome, or deliverable, of the forecast will be. In many cases, the forecaster



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement