Demand forecasting is not an easy task. Two dangers must be guarded against. First, too much emphasis should not be placed on mathematical or statistical techniques of forecasting. Though statistical techniques are essential in clarifying relationships and providing techniques of analysis, they are not substitutes for judgment. The danger is that we may go to the opposite extreme and regard forecasting as something to be left to the judgment of the so-called experts. |
Related Topic:Demand Forecasting |
Commonly for pure guessing, we can use following methods:
1. Survey of Buyer’s Intentions
A number of biases may creep into the surveys. If shortages are expected, customers may tend to exaggerate their requirements. The customers may know what their requirements are but they may misuse or mislead or may be uncertain about the quantity they intend to purchase from a particular firm. This method is not very useful in the case of household customers for several reasons, viz. irregularity in customers’ buying intentions, their inability to foresee their choice when faced with multiple alternatives, and the possibility that the buyers’ plans may not be real only wishful thinking.
2. Delphi Method
It has some exclusive advantages such as: (a) It facilitates the maintenance of anonymity of the respondent’s identity throughout the course. This enables the respondent to be candid and forth right in his view. (b) Delphi renders if possible to pose the problem to the experts at one time and have their response.
Though it posses wide knowledge and experience of the subject and have an aptitude and earnest disposition towards the participants.
3. Time Series Analysis and Trend Projection
The analyst chooses a plausible algebraic relation between sales and the independent variable such as time. The trend line is then projected into the future by extrapolation. It is popular method because it is simple and inexpensive and partly because time series data often exhibit a persistent so long as the time shows a persistent tendency to move in the some direction.
4. Market Studies and Experimentation
Market experiments have many serious shortcoming, they are expensive and are therefore usually undertaken on a scale too small to allow high levels of confidence in the results. Market experiments are seldom run for sufficiently long periods to indicate the long-run effects of various price, advertising or packaging strategies. The experimenter is thus forced to examine short run data and attempt to extend it to a longer period.
Various difficulties related with the uncontrolled parts of the market experiment also reduce its value as an estimating tool. A change in economic condition during the experiment is likely to invalidate the results, especially if the experiment includes the use of several separated markets, a local strike or layoffs by a major employer in one of the market areas. There is also the danger that customers lost during the experiment as a result of price manipulations cannot be regained when the experiment ends.
Market experimentation procedure utilizes a controlled laboratory experiment where in consumers are given funds with which to shop in a simulated store. By varying prices, product packaging, displays, and other factors, the experimenter can often learn a great deal about consumer behavior. The laboratory experiment, while providing similar information as field experiments, has an advantage because of lower cost and greater control of extraneous factors.
5. Regression Analysis
Regression analysis is to obtain accurate estimates of the variables, measures of price, credit terms, output, capacity ratios, advertising expenditures, incomes and so on. Obtaining estimates of these variables is not always easy especially if the study involves data for past years. Some key variables, such as consumer attitudes toward quality and their expectations about future business conditions – which are very important in demand functions for many consumer goods, may have to be obtained by survey techniques, which introduces on element of subjectivity into the data or by market or laboratory experiments, which may produce biased data.
6. Barometric Method
Theoretically, barometric forecasting requires the isolation of an economic time series that consistently leads the series being forecast. This relationship established; forecasting directional changes in the lagged series is simply a matter of keeping track of movement in the leading indicator. Several problems prevent such as easy solution to the forecasting problem.
- Few series always correctly indicate changes in another economic variable. Even the best leading indicators of general business conditions forecast with only to go present accuracy.
- Second, even the indicators that have good records of forecasting directional changes generally fail to lead by a consistent period. If a series is to be an adequate barometer, it not only must indicate directional changes but also, additionally, must provide a constant lead-time. Few series meet the test of lead-time consistency.
- Finally, barometric forecasting refers that even when leading indicators proved to be satisfactory from the stand point of consistently indicating directional change with a stable lead time, they provide very little information about the magnitude of change in the forecast variable.
Diffusion indexes are similar to composite indexes. Instead of combining a number of leading indicators into a single standardized index, the methodology consists of noting the percentage of the total number of leading indicators that are rising at given point in time.
Even with the use of composite and diffusion indexes, the barometric forecasting technique is a relatively poor tool for estimating the magnitude of change in an economic variable. Thus, although it represents a significant improvement over simple extrapolation techniques for short term forecasting.
7. Input-output Analysis
It is based on set of tables that describe the interrelationships among all the component and parts of the economy. Input output analysis has a variety of uses ranging from forecasting the sales of an individual firm to probing the implications of national economic programs and policies. The major contribution of input-output analysis it that it facilitates measurement of the effects on all industrial sectors that changes in activity in any one sector.
No comments:
Post a Comment