Technological forecasting - Marketing Management

The allocation of corporate funding to innovation is an investment decision that commits resources now with a view to a return in the future; yet that future is likely to be different from the circumstances that pertain today, particularly with respect to the nature of future technologies. The pace of technological change and the high risk and costs of developing new products mean that technological forecasting is essential.

What to forecast?

The first issue is the question of what we need to forecast. As with any forecast – the weather, the economy or sales forecasts – the purpose is to improve decision making. A technological forecast might be used to make better decisions in the following areas:

  • levels of research and development spending;
  • overall innovation strategy – offensive vs. defensive;
  • allocation of resources to specify innovation programmes for technological investment.

To help in such innovation-related decisions, the decision maker ideally needs to know the answers to the following:

  1. ‘What will be the nature of future technology as it relates to my business?’ A qualitative aspect.
  2. ‘What will be the performance level of future technology?’ A quantitative aspect.
  3. ‘What time-scale are we talking about; when will it happen?’ A temporal aspect.
  4. ‘What is the assessment of the likelihood of events described in the above questions?’ A probability aspect.

The information that answers these questions provides means the decision maker is in a stronger position to make informed decisions about innovation.

Techniques of technological forecasting

The techniques of technological forecasting are numerous and what follows is a brief description of the more frequently used categories, together with an indication of their merits and drawbacks.

Trend extrapolation is one of the simpler techniques that consists of using past technological trends to predict future levels of performance in a technology. Imagine that we are concerned to predict likely levels of future performance in computing technology, where ‘performance’ is measured in terms of ‘speed of calculation’. The first step is to ascertain what the past trend in this performance parameter has been, plotting past trends in this performance over time. As with the time series analysis method of sales forecasting, the performance figures over a time period will fluctuate, but beneath this is a trend which the forecaster wishes to know in order to apply the forecast. If improvements in technological performance do not follow a trend then it is not possible to forecast the future using past performance, but there is considerable evidence to show that technological progress does tend to follow a regular pattern when performance is plotted over time. The essence of the extrapolation technique is illustrated hypothetically.

In using trend extrapolation for technological forecasting, careful consideration should be given to:

  1. Selection of the parameter of performance
    Care should be taken to ensure that the performance characteristic selected is one which truly does represent the ‘correct’ measure of ‘functional’ performance, i.e. one that is not technology specific and is related to the needs of the marketplace.
  2. Sufficient and accurate historical data
    As with any forecasting technique that relies on past data to predict the future, forecasting accuracy relies on both the quantity and quality of this past data.
  3. Factors which may cause discontinuities in the shorter-term trend
    While there may be substantial continuity in the long-term progress of technological performance, in the shorter term there can be substantial discontinuity. The race to put a man on the moon by the end of the 1960s hastened the development of many technologies, as increased resources were devoted to this single aim.

Overall, the merit of technological forecasting is that it is relatively straightforward to understand and apply. The major disadvantage is that it provides only the quantitative and temporal aspects of information on new technology that the decision maker requires.

Delphi forecasting: here the forecaster recruits experts in the technology, and using a questionnaire, solicits their opinions as to likely future technological developments. The questions may relate to matters of a technological breakthrough (such as new developments in pollution-free engines or the treatment of cancer). Respondents may also be asked to predict likely time-scales, levels of performance, and estimates of probability.

Trend extrapolation forecasting

Trend extrapolation forecasting

A Delphi forecast is normally ‘played’ in a number of rounds. Once the original (first round) questionnaires have been circulated and completed, the results are summarized and then recirculated to respondents who are then asked to reconsider their forecasts in the light of summarized results. The questions themselves may become more pointed as a result of feedback. The rounds of questioning continue until a consensus emerges or sufficient useful information is available to make effective innovation decisions. Respondents do not meet face to face (as in a committee). Therefore, any ‘bandwagon’ effect of majority opinion is eliminated.

The advantages of Delphi relate to the fact that it can provide information about all areas in which the decision maker is interested. The major disadvantages are associated with difficulties in designing an unambiguous set of questions, and the selection of the panel of experts. Scenario writing first became known through the work of ‘think tanks’ such as the Hudson Institute in the United States. Now many companies have such ‘think tanks’ where a team of experts is responsible for forecasting possible future technological developments based on a wide -ranging technological and environmental analysis. A number of potential scenarios are generated, each one being considered further with respect to probabilities and implications. Scenarios considered highly probable, and with a significant projected impact on the organization (e.g. a future technological or market threat) may form the basis of a research and development programme.

Relevance trees are used systematically to explore all possible routes to achieving given stated technological objectives. The process starts by defining the desired objectives and then tracing backwards to determine possible viable routes for achieving the objective, and the implications for research and development. where the stated objective is ‘improved health care’ by a computer company that is involved in this market. A complete relevance tree will be much more complex than this simplified version.

A relevance tree for improved health care

relevance tree for improved health care

The relevance tree approach can be used:

  • to explore the feasibility of various technological decisions;
  • to establish the optimum research and development programme according to feasibility, cost and timing considerations;
  • to determine and select between detailed research projects.

These are a few of the techniques of technological forecasting. Our discussion has been brief in what is a specialist area in terms of strategies for innovation. Technological forecasting remains somewhat under-utilized in innovation planning, especially in Europe. The techniques have their limitations, but in the future more and more companies are likely to have to use them if they are to cope with an uncertain technological future.

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