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Global Trends In Techonology Management

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1.Technology forecasting

Important aspects

Primarily, a technological forecast deals with the characteristics of technology, such as levels of technical performance, like speed of a military aircraft, the power in watts of a particular future engine, the accuracy or precision of a measuring instrument, the number of transistors in a chip in the year 2015, etc. The forecast does not have to state how these characteristics will be achieved.

Secondly, technological forecasting usually deals with only useful machines, procedures or techniques. This is to exclude from the domain of technological forecasting those commodities, services or techniques intended for luxury or amusement.

Rational and explicit methods

The whole purpose of the recitation of alternatives is to show that there really is no alternative to forecasting. If a decision maker has several alternatives open to him, he will choose among them on the basis of which provides him with the most desirable outcome. Thus his decision is inevitably based on a forecast. His only choice is whether the forecast is obtained by rational and explicit methods, or by intuitive means.

The virtues of the use of rational methods are as follows:

1. They can be taught and learned,

2. They can be described and explained,

3. They provide a procedure follow able by anyone who has absorbed the necessary training, and in some cases,

4. These methods are even guaranteed to produce the same forecast regardless of who uses them.

The virtue of the use of explicit methods is that they can be reviewed by others, and can be checked for consistency. Furthermore, the forecast can be reviewed at any subsequent time.

Methods of technology forecasting

Commonly adopted methods of technology forecasting include the Delphi method, forecast by analogy, growth curves and extrapolation. Normative methods of technology forecasting -- like the relevance trees, morphological models, and mission flow diagrams -- are also commonly used.

Combining forecasts

Studies of past forecasts have shown that one of the most frequent reasons why a forecast goes wrong is that the forecaster ignores related fields.

A given technical approach may fail to achieve the level of capability forecast for it, because it is superseded by another technical approach which the forecaster ignored.

Another problem is that of inconsistency between forecasts. Because of these problems, it is often necessary to combine forecasts of different technologies. Therefore rather than to try to select the one method which is most appropriate, it may be better to try to combine the forecasts obtained by different methods.

If this is done, the strengths of one method may help compensate for the weaknesses of another.

Reasons for combining forecasts

The primary reason for combining forecasts of the same technology is to attempt to offset the weaknesses of one forecasting method with the strengths of another. In addition, the use of more than one forecasting method often gives the forecaster more insight into the processes at work which are responsible for the growth of the technology being forecast.

Trend curve and growth curves

A frequently used combination is that of growth curves and a trend curve for some technology. Here we see a succession of growth curves, each describing the level of functional capability achieved by a specific technical approach.

An overall trend curve is also shown, fitted to those items of historical data which represent the currently superior approach.

The use of growth curves and a trend curve in combination allows the forecaster to draw some conclusions about the future growth of a technology which might not be possible, were either method used alone.

Forecasts of different technologies

Combining forecasts of different technologies may be even more important than combining the forecasts of the same technology.

One reason for this is the fact that technologies may interact or be interrelated in some fashion. Another reason for this is that of consistency in an overall picture or scenario. One of the simplest examples of interacting trends is the projection to absurdity, i.e. simply projecting the given data indefinitely without getting any specific result. For instance, if one simply projects recent rates of growth of world population, one arrives at some fantastic conclusions about the density of population in a particular place by various dates in the next millennium.

Some other trends which can confidently be expected to not continue indefinitely are:

1. Annual production of scientific papers.

2. Number of automobiles per capita.



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