Archive for May, 2009

What are the main techniques of demand estimation? What is their reliability?

Demand estimation is predicting future demand form a product. The information regarding future demand is essential for planning and scheduling production, purchase of raw materials, acquision of finance and advertising.

The various techniques of demand estimation: –

  1. Survey Method

  2. Statistical Method

Survey method is generally used where the purpose is to make short run forecast of demand. Under this method, customer surveys are conducted to collect information about their intentions and future purchase plan.

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Demand Estimation: The Consumer survey method

  1. Consumer enumeration: – In this method, almost all the potential users of the product are contacted and are asked about the future plan of purchasing the product in question. The quantities indicated by the consumers are added together to obtain the probable demand for the product.

  1. Sample survey method: – Under this method only a few potential consumers selected from relevant market through a sampling method are surveyed, on the basis of the information obtained, the probable demand may be estimated through the following formula.

D = HR (H.AD)


Where D = probable demand forecast

H = Census number of households from the relevant market.

Hs = number of households reporting demand for the product.

HR = number of households reporting demand for the product.

AD = average expected consumption by the reporting households.

  1. End User Method: – The end user method of demand forecasting is used for estimating demand for inputs. Making forecast by this method requires building up a schedule of probable aggregate future demand for inputs by consuming industries and various other sectors.

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Demand Estimation: The Opinion poll Method

The opinion poll methods aim at collecting opinion of those who are supposed to possess knowledge of the market e.g. sales representative, professional marketing experts and consultants. The opinion poll method include

  1. Expert opinion method: – Firms having a good network of sales representative can put them to work of assessing the demand for the product in the areas that they represent. Sales representative, beings in close touch with the consumers are supposed to know the future purchase plans of their customer, their reaction to the market changes, their response to the introduction of new products and the demand for competing products. They are, therefore, in a position to provide an estimate of likely demand for their firm’s product in the area. The estimates of demand thus obtained from different regions are added up to get the overall probable demand for a product.

  1. Delphi Method: – Delphi method is used to consolidate the divergent expert opinions and arrived at a compromise estimate of future demand.

Under Delphi method the expert are provided information on estimates of forecast of other experts along with the underlying assumptions. The experts may revise their own estimates in the light of forecast made by other experts. The consensus of experts about the forecasts constitutes the final forecast.

Although this method is simple and inexpensive, it has its own limitations. First estimates provided by sales representations and professional experts are reliable only to extend depending upon their skill to analysis the market and their experience. Second, demand estimates way involve the subjective judgement of the which may lead to over or under estimation, finally, the assessment of market demand is usually based on inadequate information’s, such as changes in GNP, available of credit, future prospects of the industry etc, fall outside their purview.

  1. Market studies and Experiments:- It is a method of collecting necessary information regarding demand is to carry out market studies and experiments on consumer’s behavior under actual through controlled market conditions. This method is known in common parlance market conditions. This methods is known in common parlance as market experiment method under this method, firms first select some areas of the representative markets – three or four cities having similar features viz. Population, income levels, cultural and social background, occupational distribution, choices and preferences of consumers. Then, they carry out market experiments by changing prices, advt. Expenditure and other controllable variable in the demand function under the assumption that other thing remains same. The controlled variable may by changed over time either simultaneously in all the markets or in all the markets or in the selected markets. After such changes are introduced in the market, the consequent changes in the demand over a period of time (a week, a fortnight or month) are recorded. On the basis of data collected elasticity coefficient are computed. These coefficients are then used along with the variables of the demand function to assess the demand for product

The market experiments methods have certain serious limitations. First, this method is very expensive and hence cannot be afforded by small forms. Second, being a costly affair, experiments are usually carried out on a scale too small to permit generalization with a high degree of reliability.

Third experimental methods are based on short – term and controlled conditions that may exist in an uncontrolled market. Hence, the results may not be applicable to the uncontrolled long-term conditions of the market.

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Demand Estimation: The Trends Projection Method

Trend projection method is a classical method of business forecasting. This method is essentially concerned with the study of movement of variable through time. The use of this method requires a long and reliable time series data. The trend projection method is used under the assumption that the factors responsible for the past trends in variables to be projected (e.g. sales and demand) will continue to play their part in future in the same manner and to the same extend as they did in the past in determining the magnitude and direction of the variable.

There are three (3) techniques of trend projection based on time – series data.

  1. Graphical Method: – under this method, annual sales data is plotted on a graph paper and a line is drawn through the plotted points. Then a free hand line is so drawn that the total distance between the line and the point is minimum.



Trend Projection

Although this method is very simple and least expensive, the projections made through this method are not very reliable. The reason is that the extension of the trend line involves subjectivity and personal bias of the analysis.

  1. Fitting Trend Equation: Least square method: – Fitting trend equation is a formal technique of projecting the trend in demand. Under this method, a trend line (or curve) is fitted to the time – series data with the aid of statistical techniques. The form of the trend equation that can be fitted to the time series data is determined either by plotting the sales data or by trying different forms of trend equations for the best fit.

When plotted, a time series date may show various trends. The most common types of trend equation are 1) liner and 2) exponential trends

Linear Trend: – When a time series data reveals a rising trend in sales than a straight-line trend equation of the following form is fitted

S = A + BT

Where S = annual sales

T = Time (in year)

A & B are constant. The parameter b given the measure of annual increase in sales

Exponential trend:- When sales ( or any dependent variable) have increased over the past years at an increasing rate or at a constant percentage rate, than the appropriate trend equation to be used is an exponential trend equation of any of the following type

  1. Y = aebt

Or its semi – logarithmic for

Log y = = log a + bt

This form of trend equation is used when growth rate is constant.

  1. Double log trend equation of equation

Y = aTB

Or it’s double logarithmic form

Log y = log a + b log t

This form of trend equation is used when growth rate is increasing.

The first limitations of this method arise out of the assumption that the past rate of change in the dependent variable will persist in the future too. Therefore, the forecast based on this method may be considered to be reliable only for the period during which this assumption holds.

Second, this method cannot be used for short-term estimates. Also it cannot be used where trend is cyclical with sharp turning points of trough and perks.

  1. Box – Jenkins Method: – This method of forecasting is used only for short – term predictions. Besides, this method is suitable for forecasting demand with only stationary time series sales data. Stationary time series data is one, which does not reveal long term trend. In other words, Box-Jenkins technique can be used only on those cases in which time-series analysis depicts monthly or seasonal variation recurring with some degree of regularity.

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Demand Estimation: The Barometric Method

Many economists use economic indicators as barometer to forecast trends in business activities.

The basic approach of barometer technique is to construct an index of relevant economic indicators and to forecast future trends on the basis of movements in the index of economic indicators. The indicators used in this method are classified as

  1. Leading indicators: – consists of indicators which move up and down ahead of some other series e.g. new order of durable goods, new building permits etc.

  2. Coincidental indicators: – are the ones that move up and down simultaneously with the level of economic activity. E.g. number of employees in the non-agricultural sector, rate of unemployment, sales recorded by the manufacturing, trading and the retail sectors etc.

  3. Lagging indicators consists of those indicators, which follow a change after some time lag. E.g. lending rate for short-term loans etc.

Development and allotment of land by Delhi Development Authority to Group Housing Societies (a lead indicator) indicates higher demand prospects for cement, steel and other construction material (coincidental indicators) and increase in housing loan distribution (lagging indicators).

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Demand Estimation: The Econometric Method

The econometric methods combine statistical tools with economic theories to estimate economic variables and to forecast the intended economic variables. An econometric model may be single equation regression model or it may consist of a system of simultaneous equations.

Regression method

Regression analysis is the most popular method of demand estimation. This method combines economic theory and statistical techniques of estimation. Economic theory is employed to specify the determinants of demand and to determine the nature of the relationship between the demand for a product and its determinants. Economics theory thus helps in determining the general form of demand function. Statistical techniques are employed to estimate the values of parameters in the estimation equation.

Simultaneous Equation Method

It involves estimating several behavioral equations. These equations are generally behavioral equations, Mathematical equations and Market – clearing equations. The first step in this technician is to develop a complete model and specify the behavioral assumption regarding the variables included in the model. The variables that are included in the model are

1. Endogenous variables
2. Exogenous variables

Endogenous variables – the variables that are determined within the model are called endogenous variables. Endogenous variables are included in the model as depended variables that are the variables that are to be explained by the model. These are also called controlled variables. The number of equations included in the model must be equal to number of endogenous variables.

Exogenous variables – are those that are determined outside the model. Exogenous variables are inputs of the model whether a variable is treated endogenous variables or exogenous variables depend on the purpose of the model. The examples of exogenous variables are “ Money Supply”, tax rates, govt. spending etc. The exogenous variables are also known as uncontrolled variables.

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