Module 2. Theory of demand

Lesson 9

9.1 Introduction

Demand forecasting is related with future. Future is not certain and production of goods is carried out in present to be used in future. This necessitates to get an idea about future. This is carried out by the process of demand forecasting. This chapter describes process of demand forecasting.

9.2 Demand Forecasting

Demand Forecasting is a forecast is a prediction about most likely future event under assumed conditions. Demand forecasting is an estimate of future demand based upon reasonable judgment of future probabilities of events affecting business supported by scientific evidence.

9.3 Levels of Demand Forecasting

Demand forecasting can be carried out at different levels. When each individual production or service organization estimate demand for their products or services, it is called micro level demand forecasting. When demand as estimated for a group of similar production or service organizations, it is called industry level demand forecasting. When aggregate demand for industrial output by the whole country is carried out, it is called macro level demand forecasting. Macro level demand forecasting is based upon national income or aggregate expenditure of the nation.

9.4 Importance of Demand Forecasting

1. For Planning: Production involves committing resources in terms of raw materials, labour and fixed machineries etc. It is necessary to have estimate of future demand so as to avoid investments leading to excess capacity or underproduction.

2. Allocation of financial resources: Each firm has to allocate the limited funds wisely so as to ensure continuity and growth of business. Demand forecasting helps to allocate resources and aids in preparing good budget.

3. Inventory Planning: Inventory is useful but idle resource. It is necessary to keep only requisite amount of inventory avoiding unnecessary blockage of funds and ensuring continuity of production also. This is achieved by appropriate demand forecasting.

4. Future Growth Plans: Demand forecasting helps in judicious allocation of resources and aids in future expansion plans.

5. National Policy Making: Macro level demand forecasting is useful for national Planners for establishment of production capacities as well as determining export import policy.

9.5 Types of Demand Forecasting

On the basis of time horizon, the demand forecasting can be divided into following types

1. Short Term Forecasting: When forecasting is carried out for a period upto a year, it is referred to as short term forecasting. Short term forecasting is very useful to the firm for deciding its various policies related with production, pricing, purchase, finance etc.

2. Long Term Forecasting: When time period of forecasting is more than one year it is called long term forecasting. The time period may be 3 – 5 years or even more than a decade. Such forecasts are useful for long term policy making like growth, manpower planning, capital and financial planning etc.

9.6 Methods of Demand Forecasting

Different methods employed by firms for estimating demand are as follows

1. Survey Method: This is the direct method of asking the users about their preferences. Based upon the choice of respondent, it can be either consumer survey or sales force survey. Depending upon number of persons surveyed, it can be census or sample survey. In case of industrial buyers, where the number is less it is possible to survey all of them by conducting census survey. For consumer products sample survey is conducted by selecting a small number of consumers by statistical sampling method.

In case of survey methods information is obtained by directly contacting the person and conducting the interview or by using mail questionnaire. Based upon time and budget any one of the approach can be adopted. The survey method is simple but its success depends upon skill of interviewer in case of personnel interview approach and design of questionnaire in case of mail approach. In case of sales force survey, subjectivity of salesperson is a limiting factor.

2. Expert Opinion: Experts may be asked to give their estimate of the demand based upon their experience. Experts may be managers, distribution channel members, policy makers etc. This can be carried out by conducting a personnel focus group approach wherein all the experts are to meet personally at a common place and arrive at a final estimate through consensus deliberation. The other approach is referred as delphi method in which experts are asked to give their estimate but are not brought in physical contact with each other. The process includes several rounds. At the end of each round the experts are told the estimate given by others and asked to revise their earlier estimate. Anonymity is maintained during the process.

3. Experimentation: Experiments can be carried out in laboratories in controlled conditions to evaluate the consumer behaviour or in actual market environment called market test. Market test is generally used for new product wherein any past behavioral trend is not known. In test market, for a chosen market an experiment is conducted under controlled conditions by varying one or more of demand determinants such as price, promotion method etc and consumer behaviour is observed and recorded. To ensure validity, test marketing should be carried out on large population.

4. Statistical Methods: Certain statistical methods can be used to estimate future demand. They are more scientific as compared to crude value judgments. The statistical methods also should be used in combination to have better accuracy and cross checking purpose. The various methods are consumption level method, trend projections, the method of moving averages, regression analysis and econometric model building.

9.7 Statistical Methods of Demand Forecasting

Some of the statistical tools and techniques, as a part of quantitative methods for business decisions.

(1) Time series analysis or trend method : Under this method, the time series data on the under forecast are used to fit a trend line or curve either graphically or through statistical method of Least Squares. The trend line is worked out by fitting a trend equation to time series data with the aid of an estimation method. The trend equation could take either a linear or any kind of non-linear form. The trend method outlined above often yields a dependable forecast. The advantage in this method is that it does not require the formal knowledge of economic theory and the market, it only needs the time series data. The only limitation in this method is that it assumes that the past is repeated in future. Also, it is an appropriate method for long-run forecasts, but inappropriate for short-run forecasts. Sometimes the time series analysis may not reveal a significant trend of any kind. In that case, the moving average method or exponentially weighted moving average method is used to smoothen the series.

(2) Barometric Techniques or Lead-Lag indicators method : This consists in discovering a set of series of some variables which exhibit a close association in their movement over a period or time.

For example, it shows the movement of agricultural income (AY series) and the sale of tractors (ST series). The movement of AY is similar to that of ST, but the movement in ST takes place after a year’s time lag compared to the movement in AY. Thus if one knows the direction of the movement in agriculture income (AY), one can predict the direction of movement of tractors’ sale (ST) for the next year. Thus agricultural income (AY) may be used as a barometer (a leading indicator) to help the short-term forecast for the sale of tractors.

Generally, this barometric method has been used in some of the developed countries for predicting business cycles situation. For this purpose, some countries construct what are known as ‘diffusion indices’ by combining the movement of a number of leading series in the economy so that turning points in business activity could be discovered well in advance. Some of the limitations of this method may be noted however. The leading indicator method does not tell you anything about the magnitude of the change that can be expected in the lagging series, but only the direction of change. Also, the lead period itself may change overtime. Through our estimation we may find out the best-fitted lag period on the past data, but the same may not be true for the future. Finally, it may not be always possible to find out the leading, lagging or coincident indicators of the variable for which a demand forecast is being attempted.

3) Correlation and Regression : These involve the use of econometric methods to determine the nature and degree of association between/among a set of variables. Econometrics, you may recall, is the use of economic theory, statistical analysis and mathematical functions to determine the relationship between a dependent variable (say, sales) and one or more independent variables (like price, income, advertisement etc.). The relationship may be expressed in the form of a demand function, as we have seen earlier. Such relationships, based on past data can be used for forecasting. The analysis can be carried with varying degrees of complexity. Here we shall not get into the methods of finding out ‘correlation coefficient’ or ‘regression equation’; you must have covered those statistical techniques as a part of quantitative methods. Similarly, we shall not go into the question of economic theory. We shall concentrate simply on the use of these econometric techniques in forecasting.

We are on the realm of multiple regression and multiple correlation. The form of the equation may be:

DX = a + b1 A + b2PX + b3Py

You know that the regression coefficients b1, b2, b3 and b4 are the components of relevant elasticity of demand. For example, b1 is a component of price elasticity of demand. The reflect the direction as well as proportion of change in demand for x as a result of a change in any of its explanatory variables. For example, b2< 0 suggest that DX and PX are inversely related; b4 > 0 suggest that x and y are substitutes; b3 > 0 suggest that x is a normal commodity with commodity with positive income-effect.

Given the estimated value of and bi, you may forecast the expected sales (DX), if you know the future values of explanatory variables like own price (PX), related price (Py), income (B) and advertisement (A). Lastly, you may also recall that the statistics R2 (Co-efficient of determination) gives the measure of goodness of fit. The closer it is to unity, the better is the fit, and that way you get a more reliable forecast.

The principle advantage of this method is that it is prescriptive as well descriptive. That is, besides generating demand forecast, it explains why the demand is what it is. In other words, this technique has got both explanatory and predictive value. The regression method is neither mechanistic like the trend method nor subjective like the opinion poll method. In this method of forecasting, you may use not only time-series data but also cross section data. The only precaution you need to take is that data analysis should be based on the logic of economic theory.

(4) Simultaneous Equations Method : Here is a very sophisticated method of forecasting. It is also known as the ‘complete system approach’ or ‘econometric model building’. In your earlier units, we have made reference to such econometric models. Presently we do not intend to get into the details of this method because it is a subject by itself. Moreover, this method is normally used in macro-level forecasting for the economy as a whole; in this course, our focus is limited to micro elements only. Of course, you, as corporate managers, should know the basic elements in such an approach.

The method is indeed very complicated. However, in the days of computer, when package programmes are available, this method can be used easily to derive meaningful forecasts. The principle advantage in this method is that the forecaster needs to estimate the future values of only the exogenous variables unlike the regression method where he has to predict the future values of all, endogenous and exogenous variables affecting the variable under forecast. The values of exogenous variables are easier to predict than those of the endogenous variables. However, such econometric models have limitations, similar to that of regression method.
Last modified: Saturday, 29 September 2012, 10:17 AM