Market research studies can sometimes be supplemented by referring to the performance of an ancestor of the product or service under consideration and applying an analysis of the product life cycle curve. For example, the assumption can be made that color television would follow the general sales pattern experienced with black and white television but that it would take twice as long to reach a steady state. Such comparisons provide guidelines during initial planning phases and may be supplemented by other kinds of analyses and studies as actual demand becomes known. A few experts focus their attention on the life cycle of products in studying the problems of production management.
Scenario Based Forecasting:
Long range forecasting has been criticized because of its lack of predictive accuracy. Ascher (1978) has documented that the results of long range forecasts in several areas have been inaccurate because the core assumption upon which the forecasts were predicted proved to be wrong. It is reasonable to assume that the further the forecast horizon, the less accurate the forecast is likely to be. Long range forecasts, however, are an integral part of long range planning, so they must be made.
The approach that we discuss now utilizes multiple future scenarios to come up with alternative projections. The decision maker is provided with the conditions under which high or low forecasts would result.
The first step in this approach is to define the variable to be projected and establish its measurement unit. Since a multitude of factors influence the variable Y, the next steps is to identify these factors. Suppose X1, X2…. Xn are the factors that influence Y. An example of Y might be the capacity, measured in megawatts, of solar electric energy sources installed by the year 2000. Examples of X factors might be
X1= Cost of solar energy in the year 2000
X2 = Demand for energy
X3 = Cost of nuclear, coal, and oil energies
X4= Oil embargo
X5= Nuclear slowdown
X6= Trend of society toward decentralization
The selected factors X1, X2…., Xn must be comprehensive enough to reflect all the relevant concerns about the future and must also be well defined.
Given a combination of some specific levels of these factors, the projection for Y may have to be made by consulting experts or utilizing past data and analogies. Techniques of regression analysis can be used to establish the relationship between Y and X1, X2, … Xn. The projection for Y will now vary with the combination of the levels of the factors that is assumed, which is called a scenario. Managers mat also be interested in knowing which future scenarios are more likely to occur. This would require assessing the probability of occurrence of a certain level of a factor and then combining these for all the factors to come up with a scenario probability For example, if there are only two factors and each factor has only two levels that is, each factor either occurs or does not occur then there are four possible scenarios. If the probability of occurrence for the first factor is 0.8 and that for the second factor is 0.5 then the probability of the scenario consisting of the occurrence of both factors will be 0.8 x 0.5 = 0.4. Of course it is implicitly assumed that the probability of the occurrence of the two factors is independent. More sophisticated approaches will be required to quantify scenario probabilities if the factors are interdependent.