Technological Forecasting Model
Technological forecasting is a subset of futures research. Futures research is an umbrella term which encompasses "any activity that improves understanding about the future consequences of present developments and choices" (Amara and Salanik, 1972, p. 415). In defining forecasting, the authors offer the following progression. Forecasting is:
- A statement about the future,
- A probabilistic statement about the future:
- A probabilistic, reasonably definite statement about the future:
- A probabilistic, reasonably definite statement about the future, based upon an evaluation of alternative possibilities. (Amara and Salanik, 1972, p. 415)
Technological forecasting includes "all efforts to project technological capabilities and to predict the invention and spread of technological innovations" (Ascher, 1979, p. 165). Martino (1983) states that a technological forecast includes four elements: the time of the forecast or the future date when the forecast is to be realized, the technology being forecast, the characteristics of the technology or the functional capabilities of the technology, and a statement about probability (Donnelly, n.d.).
Forecasting a technology is a difficult task and includes "the uncertainty and unreliability of data, the complexity of 'real world' feedback interactions, the temptation of wishful or emotional thinking, the fatal attraction of ideology, [and] the dangers of forcing soft and somewhat pliable 'facts' into a preconceived pattern" (Ayers, 1969, p. 18).
These methods also rely on judgment and are particularly appropriate for very new technologies and very long-range forecasting. The following are examples of this model (click on each for further information):
- Curve Fitting
- Extrapolation of Trends
- S-shaped Curves
- Envelope S-Curves
- Analogous Data
In general, as a technology moves from the early stages of laboratory development to widespread acceptance in the marketplace, the forecasting methodologies that are most appropriate move from qualitative to quantitative techniques. Since technological forecasting is employed to predict long-term technological developments, the methods used are generally qualitative (Donnelly, n.d.).
Trend Extrapolation
A forecast can be generated by "observing a change through time in the character of something and projecting or extrapolating that change into the future" (Cornish, 1977, p. 108). In making such a forecast, the focus is on the long-term trend, so short-term fluctuations are disregarded. Trend extrapolations require that the forecaster have an understanding of the factors which contributed to change in the past, and possess confidence in the notion that these factors will continue to influence developments in a similar fashion in the future (Schwarz, Svedin, Wittrock, 1982, p. 20).
One commonly employed approach to trend extrapolation involves the use of growth curves (Cornish, 1977, pp. 110-111). Growth curves are loosely based upon the notion that the growth of a technology can be charted in the same way organic growth can be charted. For example, the growth in height and weight of an individual can be charted, and will commonly display a pattern which indicates a leveling off around early adulthood (Donnelly, n.d.). As an illustration, Martino (1983) describes how this particular technique can be used in charting and forecasting the growth in, and leveling off, of the number of cable television subscribers (Donnelly, n.d.).
Regarding the accuracy of trend extrapolation as a forecasting technique, Ascher (1978) questions its "objectivity and reliability" (p. 183). Schnaars (1989) goes even further and admonishes forecasters to discount trend extrapolations. Schnaars notes that trends and patterns have no life of their own and are susceptible to sudden changes, and that focusing on trends alone "is often a search for the will-o'-the wisp" (p. 152). As an example of a misuse of trend extrapolation, Schnaars notes the actions taken by American electronics firms with regard to television manufacturing. Through the 1950s and the 1960s, television sets steadily grew larger. As American firms continued to make large, cabinet-based systems, Japanese firms began to concentrate on making portable sets (Donnelly, n.d.).
Delphi Forecasting Model
Delphi Analysis is used in the decision-making process, in particular in forecasting. Several "experts" sit together and try to compromise on something upon which they cannot agree. In fact, the Delphi procedure is designed for the systematic solicitation of expert opinion.
Many things can influence Opinions in-group settings, including the dominant positions of some participants, personal magnetism, "alleged expertise", and fringe opinions.
The Delphi technique is a method of obtaining what could be considered an intuitive consensus of group expert opinions. The accuracy of the forecast produced is limited by the quality of opinions provided by the experts, and it should be noted that some authors (such as, Challis and Wills, 1970 and Wise, 1976) have questioned the accuracy of the opinions of specialists (Donnelly, n.d.).
Benefits
- Eliminates need for group meetings.
- Alleviates some of the bias inherent in group meetings.
- Participants can change their minds anonymously.
- When attempting to forecast demands for a new item these techniques are more useful as there is no previous history
- It may be more appropriate to relate the products only qualitatively in order to get an impression of demand patterns or aggregate demand if the relationship is not direct between them.
- When substantial data are lacking, subjective management judgment may be the better alternative.
Weaknesses
- Can take a great deal of time to reach consensus.
- Participants may drop out.
- Data gathered by these methods should be considered in some aggregate inventory or capacity planning decisions, but should not be the sole data source for such decisions.
- The Delphi technique is not a suitable technique for short-range forecasting, certainly not for individual products.
- They are not intended directly to support inventory decisions. Rather, they are intended to support product development and promotion strategies.
For the information services company in the previous example, mainframe computer forecasting using the Delphi method would be conducted by having the Service director (1) ask all participants to anonymously submit forecast estimates, (2) tabulate the results, (3) return these tabulated results to the participants, telling them to what extent there was general consensus, and asking them to state their reasons for any widely divergent estimates they had made and resubmit an updated anonymous forecast estimate, (4) cycle through stages (2)-(4) until a general consensus emerges (Anonymous, 2000-2006).
Time-Series Forecasting Method
Time series is one of the quantitative methods used to determine patterns in data collected over time. Time series analysis is used to detect pattern of change in statistical information over regular intervals of time. Time series is used to project these patterns to arrive at estimate for the future. Thus, Time-series helps us cope with uncertainty about the future. The limitation is it is dependent on accuracy of data. Time-series may be affected by various variables, which may not show the true picture. Time-series may be time-consuming and expensive.
These models are based only on past data. The focus is on using patterns, changes, disturbances in the data to forecast the future. Four types of Time Series Models include:
- Moving Averages
- Exponential Smoothing
- Decomposition Models
- Box-Jenkins
Decomposition Models
This forecasting approach is based on the idea that a forecast can be improved if the underlying factors of a data pattern can be identified and forecasted separately. Breaking down the data into its component parts is called decomposition. The decomposition model assumes that sales are affected by four factors: the general trend in the data, general economic cycles, seasonality, and irregular or random occurrences. The forecast is made by considering each of these components separately and combining them together.
Example – Cable TV Broadcasting
The company uses a combines approach to forecasting. All market forecasts are based on statistical forecasting techniques based on historic performance (linear extrapolation of the market size, based on the five-year historical growth). However, these statistical tools are supplemented with qualitative parameters such as: industry expectation/ opinion, socio-economic drivers, new product development, technological advances, expected levels of market saturation (Anonymous, 2001).
General definitions: Please find below an explanation of terms used throughout Snapshot reports.
Compound annual growth rate
CAGR =. This is a formula to measure the annual growth rate of a market over a period of several years. CAGR growth rate is the constant percentage rate at which a market would have to grow, year on year, to reach its current value (y) from the value in a base year (x). Compared to average growth rate this is a more representative measure of growth. CAGR is calculated using the formula ((y/x)^(l/n))-1 where ‘^’ denotes ‘to the power of’, y is the value of the market in the final period covered, x is the value in the first year and n is the number of years included in the calculation (Anonymous, 2001).
Market Value
Market Value = All market values are expressed at either retail selling prices (RSP) or other measures as specified in the reports.
Market Volume
Market Volume = All market volumes are expressed in the unit relevant to the market researched (i.e. kg, litres).
The reports also provide market forecasts to the year 2005. The forecast are based on projected market volume and does take into account-projected inflation (Anonymous, 2001).
In other words, the time series models were combined with judgmental and technological approaches.
Conclusion
Accordingly, the forecasting model or method must adapt to reality; it is futile to attempt to adapt reality to the model. As representations, models cannot be exact. Models imply that action is taken only after careful thought and reflection. This can have major consequences in the financial realm. A key element of financial planning and financial forecasting is the ability to construct models or methods showing the interrelatedness of financial data. Models showing correlation or causation between variables can be used to improve financial decision-making.
References
Amara, R. and Salanik, G. (1972). Forecasting: From conjectural art toward science.
Technological Forecasting and Social Change, 3 (3), pp.415-426,
Retrieved August 24, 2006.
Anonymous (2000-2006). Judgement Models: Delphi Method,
Retrieved August 25, 2006 from the worldwide website:
Anonymous (2001). Western Europe Carbonated Soft Drinks, Retrieved August 26, 2006
from the worldwide website:
Ascher, W. (1978). Forecasting: An appraisal for policymakers and planners. Baltimore: Johns
Hopkins University Press, p.183, Retrieved August 25, 2006.
Ayers, R. (1969). Technological forecasting and long-range planning. New York: McGraw-Hill
Book Company, Retrieved August 24, 2006.
Challis, A. & Wills, G. (1970). Technological forecasting. In D. Ashton & L. Simister (Eds.)
The role of forecasting in corporate planning, pp. 100- 124. London: Staples Press,
Retrieved August 25, 2006.
Cornish, E. (1977). The study of the future. Washington, D.C.: World Future Society,
pp.108-111, Retrieved August 25, 2006.
Donnelly David Dr. (n.d.).Forecasting Methods: A Selective Literature Review,
Retrieved August 26, 2006 from the worldwide website:
Hossein Arsham (1994-2006). Time-Critical Decision Making for Business Administration,
pp.1-152, Retrieved August 25, 2006 from the worldwide website:
Martino, J. (1983). Technological forecasting for decision making. New York: Elsevier Science
Publishing Company, Retrieved August 24, 2006.
Schnaars, S. (1989). Megamistakes: Forecasting and the myth of rapid technological change.
New York: The Free Press, p.152, Retrieved August 24, 2006.
Schwarz, B., Svedin, U. & Wittrock, B. (1982). Methods in future studies. Boulder, Colorado:
Westview Press, p.20, Retrieved August 25, 2006.
